Journal articles on the topic 'Medical sciences Computer network resources'
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Peisert, Sean, Eli Dart, William Barnett, Edward Balas, James Cuff, Robert L. Grossman, Ari Berman, Anurag Shankar, and Brian Tierney. "The medical science DMZ: a network design pattern for data-intensive medical science." Journal of the American Medical Informatics Association 25, no. 3 (October 6, 2017): 267–74. http://dx.doi.org/10.1093/jamia/ocx104.
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Abstract Objective We describe a detailed solution for maintaining high-capacity, data-intensive network flows (eg, 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations. Materials and Methods High-end networking, packet-filter firewalls, network intrusion-detection systems. Results We describe a “Medical Science DMZ” concept as an option for secure, high-volume transport of large, sensitive datasets between research institutions over national research networks, and give 3 detailed descriptions of implemented Medical Science DMZs. Discussion The exponentially increasing amounts of “omics” data, high-quality imaging, and other rapidly growing clinical datasets have resulted in the rise of biomedical research “Big Data.” The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large datasets. Maintaining data-intensive flows that comply with the Health Insurance Portability and Accountability Act (HIPAA) and other regulations presents a new challenge for biomedical research. We describe a strategy that marries performance and security by borrowing from and redefining the concept of a Science DMZ, a framework that is used in physical sciences and engineering research to manage high-capacity data flows. Conclusion By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements.
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Peisert, Sean, William Barnett, Eli Dart, James Cuff, Robert L. Grossman, Edward Balas, Ari Berman, Anurag Shankar, and Brian Tierney. "The Medical Science DMZ." Journal of the American Medical Informatics Association 23, no. 6 (May 2, 2016): 1199–201. http://dx.doi.org/10.1093/jamia/ocw032.
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Abstract Objective We describe use cases and an institutional reference architecture for maintaining high-capacity, data-intensive network flows (e.g., 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations. Materials and Methods High-end networking, packet filter firewalls, network intrusion detection systems. Results We describe a “Medical Science DMZ” concept as an option for secure, high-volume transport of large, sensitive data sets between research institutions over national research networks. Discussion The exponentially increasing amounts of “omics” data, the rapid increase of high-quality imaging, and other rapidly growing clinical data sets have resulted in the rise of biomedical research “big data.” The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large data sets. Maintaining data-intensive flows that comply with HIPAA and other regulations presents a new challenge for biomedical research. Recognizing this, we describe a strategy that marries performance and security by borrowing from and redefining the concept of a “Science DMZ”—a framework that is used in physical sciences and engineering research to manage high-capacity data flows. Conclusion By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements.
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Minaeva, N. V., and D. M. Shiryaeva. "Pollen allergy and supporting information resources." Russian Medical Inquiry 5, no. 1 (2021): 38–42. http://dx.doi.org/10.32364/2587-6821-2021-5-1-38-42.
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Dramatic progress of computer technologies triggered the development of global information space covering almost all areas of human activity. Medical sciences including allergy were no exception. Pollen allergy is a common condition characterized by regional and geographic specifics. Current modalities provide distance relationship between the doctor and patient with pollen allergy, optimize treatment and prevention of exacerbations, and improve the quality of life. There are three types of online resources for patients with pollen allergy, i.e., (1) pollen monitoring and pollen count measurements, (2) pollen allergy clinical sign maps, and (3) resources predicting the risk of exacerbations which contain additional important information. This paper addresses current Russian and European internet resources for patients with pollen allergy. European information programs are a part of public health system being a single network that functions stably for many decades. Domestic online resources have recently appeared and their functioning is unstable. However, these online resources are characterized by promising future development. KEYWORDS: hay fever, pollen allergy, pollen monitoring, symptom, prediction, information resources, internet. FOR CITATION: Minaeva N.V., Shiryaeva D.M. Pollen allergy and supporting information resources. Russian Medical Inquiry. 2021;5(1):38–42. DOI: 10.32364/2587-6821-2021-5-1-38-42.
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Ghani, Arfan, Rawad Hodeify, Chan H. See, Simeon Keates, Dah-Jye Lee, and Ahmed Bouridane. "Computer Vision-Based Kidney’s (HK-2) Damaged Cells Classification with Reconfigurable Hardware Accelerator (FPGA)." Electronics 11, no. 24 (December 19, 2022): 4234. http://dx.doi.org/10.3390/electronics11244234.
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In medical and health sciences, the detection of cell injury plays an important role in diagnosis, personal treatment and disease prevention. Despite recent advancements in tools and methods for image classification, it is challenging to classify cell images with higher precision and accuracy. Cell classification based on computer vision offers significant benefits in biomedicine and healthcare. There have been studies reported where cell classification techniques have been complemented by Artificial Intelligence-based classifiers such as Convolutional Neural Networks. These classifiers suffer from the drawback of the scale of computational resources required for training and hence do not offer real-time classification capabilities for an embedded system platform. Field Programmable Gate Arrays (FPGAs) offer the flexibility of hardware reconfiguration and have emerged as a viable platform for algorithm acceleration. Given that the logic resources and on-chip memory available on a single device are still limited, hardware/software co-design is proposed where image pre-processing and network training were performed in software, and trained architectures were mapped onto an FPGA device (Nexys4DDR) for real-time cell classification. This paper demonstrates that the embedded hardware-based cell classifier performs with almost 100% accuracy in detecting different types of damaged kidney cells.
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Zambrano-Vizuete, Marcelo, Miguel Botto-Tobar, Carmen Huerta-Suárez, Wladimir Paredes-Parada, Darwin Patiño Pérez, Tariq Ahamed Ahanger, and Neilys Gonzalez. "Segmentation of Medical Image Using Novel Dilated Ghost Deep Learning Model." Computational Intelligence and Neuroscience 2022 (August 12, 2022): 1–9. http://dx.doi.org/10.1155/2022/6872045.
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Image segmentation and computer vision are becoming more important in computer-aided design. A computer algorithm extracts image borders, colours, and textures. It also depletes resources. Technical knowledge is required to extract information about distinctive features. There is currently no medical picture segmentation or recognition software available. The proposed model has 13 layers and uses dilated convolution and max-pooling to extract small features. Ghost model deletes the duplicated features, makes the process easier, and reduces the complexity. The Convolution Neural Network (CNN) generates a feature vector map and improves the accuracy of area or bounding box proposals. Restructuring is required for healing. As a result, convolutional neural networks segment medical images. It is possible to acquire the beginning region of a segmented medical image. The proposed model gives better results as compared to the traditional models, it gives an accuracy of 96.05, Precision 98.2, and recall 95.78. The first findings are improved by thickening and categorising the image’s pixels. Morphological techniques may be used to segment medical images. Experiments demonstrate that the recommended segmentation strategy is effective. This study rethinks medical image segmentation methods.
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Wang, Beibei, Binyu Yan, Gwanggil Jeon, Xiaomin Yang, Changjun Liu, and Zhuoyue Zhang. "Lightweight Dual Mutual-Feedback Network for Artificial Intelligence in Medical Image Super-Resolution." Applied Sciences 12, no. 24 (December 13, 2022): 12794. http://dx.doi.org/10.3390/app122412794.
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As a result of hardware resource constraints, it is difficult to obtain medical images with a sufficient resolution to diagnose small lesions. Recently, super-resolution (SR) was introduced into the field of medicine to enhance and restore medical image details so as to help doctors make more accurate diagnoses of lesions. High-frequency information enhances the accuracy of the image reconstruction, which is demonstrated by deep SR networks. However, deep networks are not applicable to resource-constrained medical devices because they have too many parameters, which requires a lot of memory and higher processor computing power. For this reason, a lightweight SR network that demonstrates good performance is needed to improve the resolution of medical images. A feedback mechanism enables the previous layers to perceive high-frequency information of the latter layers, but no new parameters are introduced, which is rarely used in lightweight networks. Therefore, in this work, a lightweight dual mutual-feedback network (DMFN) is proposed for medical image super-resolution, which contains two back-projection units that operate in a dual mutual-feedback manner. The features generated by the up-projection unit are fed back into the down-projection unit and, simultaneously, the features generated by the down-projection unit are fed back into the up-projection unit. Moreover, a contrast-enhanced residual block (CRB) is proposed as each cell block used in projection units, which enhances the pixel contrast in the channel and spatial dimensions. Finally, we designed a unity feedback to down-sample the SR result as the inverse process of SR. Furthermore, we compared it with the input LR to narrow the solution space of the SR function. The final ablation studies and comparison results show that our DMFN performs well without utilizing a large amount of computing resources. Thus, it can be used in resource-constrained medical devices to obtain medical images with better resolutions.
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Amalraj, Jansi Rani, and Robert Lourdusamy. "Security and privacy issues in federated healthcare – An overview." Open Computer Science 12, no. 1 (January 1, 2022): 57–65. http://dx.doi.org/10.1515/comp-2022-0230.
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Abstract Securing medical records is a significant task in Healthcare communication. The major setback during the transfer of medical data in the electronic medium is the inherent difficulty in preserving data confidentiality and patients’ privacy. The innovation in technology and improvisation in the medical field has given numerous advancements in transferring the medical data with foolproof security. In today’s healthcare industry, federated network operation is gaining significance to deal with distributed network resources due to the efficient handling of privacy issues. The design of a federated security system for healthcare services is one of the intense research topics. This article highlights the importance of federated learning in healthcare. Also, the article discusses the privacy and security issues in communicating the e-health data.
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Ahmad, Mubashir, Syed Furqan Qadri, Salman Qadri, Iftikhar Ahmed Saeed, Syeda Shamaila Zareen, Zafar Iqbal, Amerah Alabrah, Hayat Mansoor Alaghbari, and Sk Md Mizanur Rahman. "A Lightweight Convolutional Neural Network Model for Liver Segmentation in Medical Diagnosis." Computational Intelligence and Neuroscience 2022 (March 30, 2022): 1–16. http://dx.doi.org/10.1155/2022/7954333.
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Liver segmentation and recognition from computed tomography (CT) images is a warm topic in image processing which is helpful for doctors and practitioners. Currently, many deep learning methods are used for liver segmentation that takes a long time to train the model which makes this task challenging and limited to larger hardware resources. In this research, we proposed a very lightweight convolutional neural network (CNN) to extract the liver region from CT scan images. The suggested CNN algorithm consists of 3 convolutional and 2 fully connected layers, where softmax is used to discriminate the liver from background. Random Gaussian distribution is used for weight initialization which achieved a distance-preserving-embedding of the information. The proposed network is known as Ga-CNN (Gaussian-weight initialization of CNN). General experiments are performed on three benchmark datasets including MICCAI SLiver’07, 3Dircadb01, and LiTS17. Experimental results show that the proposed method performed well on each benchmark dataset.
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Siddike, Md Abul Kalam, and Md Shiful Islam. "Acceptance of E-Resources by the Medical Researchers of International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B)." Journal of Information & Knowledge Management 13, no. 02 (June 2014): 1450012. http://dx.doi.org/10.1142/s0219649214500129.
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The purpose of this paper is to describe the acceptance of e-resources by the medical researchers of International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B) and its objective is to explore the tendency and frequency of using e-resources by them. Also, this article investigates the purposes, impact, influential factors and barriers of using e-resources by the medical researchers of ICDDR,B. A survey has been conducted by using a short and well-structured questionnaire that was distributed among 120 medical researchers in ICDDR,B. We received 96 questionnaires duly filled up by the respondents with a response rate of 79.99%. The gathered data have been analysed and presented using the descriptive analysis techniques of SPSS 20.0. Findings show that the medical researchers of ICDDR,B show positive attitude towards using e-resources, and researchers use e-resources frequently. Results also indicate that e-resources are very useful to the medical researchers, and MEDLINE is the most used database among the medical researchers of ICDDR,B. The study is based on only ICDDR,B and does not cover all medical (public and private) institutions of Bangladesh. Therefore, further studies would be carried out covering public and private medical institutions in Bangladesh.
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Kim, Meen Chul, Seojin Nam, Fei Wang, and Yongjun Zhu. "Mapping scientific landscapes in UMLS research: a scientometric review." Journal of the American Medical Informatics Association 27, no. 10 (October 1, 2020): 1612–24. http://dx.doi.org/10.1093/jamia/ocaa107.
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Abstract Objective The Unified Medical Language System (UMLS) is 1 of the most successful, collaborative efforts of terminology resource development in biomedicine. The present study aims to 1) survey historical footprints, emerging technologies, and the existing challenges in the use of UMLS resources and tools, and 2) present potential future directions. Materials and Methods We collected 10 469 bibliographic records published between 1986 and 2019, using a Web of Science database. graph analysis, data visualization, and text mining to analyze domain-level citations, subject categories, keyword co-occurrence and bursts, document co-citation networks, and landmark papers. Results The findings show that the development of UMLS resources and tools have been led by interdisciplinary collaboration among medicine, biology, and computer science. Efforts encompassing multiple disciplines, such as medical informatics, biochemical sciences, and genetics, were the driving forces behind the domain’s growth. The following topics were found to be the dominant research themes from the early phases to mid-phases: 1) development and extension of ontologies and 2) enhancing the integrity and accessibility of these resources. Knowledge discovery using machine learning and natural language processing and applications in broader contexts such as drug safety surveillance have recently been receiving increasing attention. Discussion Our analysis confirms that while reaching its scientific maturity, UMLS research aims to boundary-span to more variety in the biomedical context. We also made some recommendations for editorship and authorship in the domain. Conclusion The present study provides a systematic approach to map the intellectual growth of science, as well as a self-explanatory bibliometric profile of the published UMLS literature. It also suggests potential future directions. Using the findings of this study, the scientific community can better align the studies within the emerging agenda and current challenges.
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Bradai, Nourchene, Lamia Chaari, and Lotfi Kamoun. "A Comprehensive Overview of Wireless Body Area Networks (WBAN)." International Journal of E-Health and Medical Communications 2, no. 3 (July 2011): 1–30. http://dx.doi.org/10.4018/jehmc.2011070101.
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In recent years, the wireless body area network (WBAN) has emerged as a new technology for e-healthcare applications. The WBANs promise to revolutionize health monitoring. However, this technology remains in the first stages and much research is underway. Designers of such systems face a number of challenging tasks, as they need to address conflicting requirements. This includes managing the network, the data, while maximizing the autonomy of each network node. Reducing the consumption of a node, the management of network resources and security insurance are therefore major challenges. This paper presents a survey of body area networks including the WBANs challenges and -architecture, the most important body sensor devices, as well as sensor board hardware and platforms. Further, various applications of WBANs in the medical field are discussed, as well as wireless communications standards and technologies. The newest researches related to WBANs at physical and MAC layers are presented. Finally the paper identifies data security and privacy in WBANs as well as open research issues.
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Sugadev, M., Sonia Jenifer Rayen, J. Harirajkumar, R. Rathi, G. Anitha, S. Ramesh, and Kiran Ramaswamy. "Implementation of Combined Machine Learning with the Big Data Model in IoMT Systems for the Prediction of Network Resource Consumption and Improving the Data Delivery." Computational Intelligence and Neuroscience 2022 (July 19, 2022): 1–12. http://dx.doi.org/10.1155/2022/6510934.
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In recent years, health applications based on the internet of medical things have exploded in popularity in smart cities (IoMT). Many real-time systems help both patients and professionals by allowing remote data access and appropriate responses. The major research problems include making timely medical judgments and efficiently managing massive data utilising IoT-based resources. Furthermore, in many proposed solutions, the dispersed nature of data processing openly raises the risk of information leakage and compromises network integrity. Medical sensors are burdened by such solutions, which reduce the stability of real-time transmission systems. As a result, this study provides a machine-learning approach with SDN-enabled security to forecast network resource usage and enhance sensor data delivery. With a low administration cost, the software define network (SDN) design allows the network to resist dangers among installed sensors. It provides an unsupervised machine learning approach that reduces IoT network communication overheads and uses dynamic measurements to anticipate link status and refines its tactics utilising SDN architecture. Finally, the SDN controller employs a security mechanism to efficiently regulate the consumption of IoT nodes while also protecting them against unidentified events. When the number of nodes and data production rate varies, the suggested approach enhances network speed. As a result, to organise the nodes in a cluster, the suggested model uses an iterative centroid technique. By balancing network demand via durable connections, the multihop transmission technique for routing IoT data improves speed while simultaneously lowering the energy hole problem.
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Bouwmeester, Carla J. "Surveying Physicians' Attitudes about Herbal Supplements, Resources, and Pharmacy Consultations." Journal of Pharmacy Technology 21, no. 5 (September 2005): 247–53. http://dx.doi.org/10.1177/875512250502100502.
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Objective: To investigate whether physicians discuss herbal supplement use with their patients and document this information in the medical record, to determine the perceived barriers to discussing herbal supplement use, and to assess all resources currently available to physicians in their office setting and additional resources needed to answer questions about herbal supplements. Methods: An electronic survey was conducted of physicians enrolled in a managed care electronic network as of November 2002; data were collected from December 2002 through March 2003. Results: Of the 203 physicians who responded to the survey, 18% always discussed herbal supplements with their patients, 57% sometimes carried on these discussions, 21% rarely did, and 4% never asked. These responses correlated roughly with how often herbal supplement use was documented in the medical chart (always 27%, sometimes 51%, rarely 20%, never 2%). The strongest barriers to discussing herbal supplements were lack of resources and lack of time. The largest number of physicians used Web sites or print resources for information on herbal supplements. The most preferred resources were Web sites, computer databases, and pharmacy consultations. Conclusions: Awareness of herbal supplement use is vital for the healthcare practitioner to deliver comprehensive health services. Physicians' attitudes regarding herbal supplements are influenced by the resources available and by personal bias. Pharmacists can play a pivotal role in providing consultation services, educational materials, and screening for drug–herb interactions.
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Teng, Hui. "Construction and Drug Evaluation Based on Convolutional Neural Network System Optimized by Grey Correlation Analysis." Computational Intelligence and Neuroscience 2021 (September 15, 2021): 1–9. http://dx.doi.org/10.1155/2021/2794588.
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Incidence rate of mental illness is increasing year by year with the development of city. The amount of modern medical data is huge and complex. In many cases, it is difficult to realize the rational allocation of resources, which puts forward an urgent demand for the artificial intelligence of modern medicine and brings great pressure to the development of the medical industry. The purpose of this study is to develop and construct a grey correlation analysis and related drug evaluation system of mental diseases based on deep convolution neural network. The establishment of the system can effectively improve the automation and intelligence of modern psychiatric treatment process. In this article, the grey correlation analysis of patient data is carried out, and then, the optimized deep convolution neural network is constructed. Combined with the medical knowledge base, the analysis of disease results is realized, and on this basis, the efficacy of related drugs in the treatment of mental diseases is evaluated. The results show that the advantage of the deep convolution neural network system is to effectively improve the induction rate. What’s more, compared with other algorithms, this algorithm has higher accuracy and efficiency. It improves the comprehensiveness and informatization of disease screening methods, improves the accuracy of screening, reduces the consumption of doctors’ human resources, and provides a theoretical basis for the digitization of the medical industry in the future.
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Feraru, Silvia Monica, Horia-Nicolai Teodorescu, and Marius Dan Zbancioc. "SRoL - Web-based Resources for Languages and Language Technology e-Learning." International Journal of Computers Communications & Control 5, no. 3 (September 1, 2010): 301. http://dx.doi.org/10.15837/ijccc.2010.3.2483.
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The SRoL Web-based spoken language repository and tool collection includes thousands of voice recordings grouped on sections like "Basic sounds of the Romanian language", "Emotional voices", "Specific language processes", "Pathological voices", "Comparison of natural and synthetic speech", "Gnathophonics and gnathosonics". The recordings are annotated and documented according to proprietary methodology and protocols. Moreover, we included on the site extended documentation on the Romanian language, on speech technology, and on tools, produced by the SRoL team, for voice analysis. The resources are a part of the CLARIN European Network for Language Resources. The resources and tools are useful in virtual learning for phonetics of the Romanian language, speech technology, and medical subjects related to voice. We report on several applications in language learning and voice technology classes. Here, we emphasize the utilization of the SRoL resources in education for medicine and speech rehabilitation.
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Yan, Yongjie, Guang Yu, and Xiangbin Yan. "Online Doctor Recommendation with Convolutional Neural Network and Sparse Inputs." Computational Intelligence and Neuroscience 2020 (October 15, 2020): 1–10. http://dx.doi.org/10.1155/2020/8826557.
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The recommendation system in the online medical consultation website is a system to assist patients to find appropriate doctors. Based on the analysis of the current situation of the development of an online medical community (Haodf.com) in China, this paper puts forward recommendation suggestions of finding the right hospital and doctor to promote the rapid integration of Internet technology and traditional medical services. A new recommendation model called Probabilistic Matrix Factorization integrated with Convolutional Neural Network (PMF-CNN) is proposed in the paper. Doctors’ data in Haodf.com were used to evaluate the performance of our system. The model improves the performance of medical consultation recommendations by fusing review text and doctor information based on CNN (Convolutional Neural Network). Specifically, CNN is used to learn the feature representation of the review text and the doctors’ information. Furthermore, the extended matrix factorization model is exploited to fuse the review information feature and the initial value of the doctors’ information for recommendation. As is shown in the experimental results on Haodf.com datasets, the proposed PMF-CNN achieves better recommendation performances than the other state-of-the-art recommendation algorithms. And the recommendation system in an online medical website improves the utilization efficiency of doctors and the balance of public health resources allocation.
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Ma, Lina, and Tao Yang. "Construction and Evaluation of Intelligent Medical Diagnosis Model Based on Integrated Deep Neural Network." Computational Intelligence and Neuroscience 2021 (November 25, 2021): 1–10. http://dx.doi.org/10.1155/2021/7171816.
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In recent years, as human life expectancy increases, birth rate decreases and health management concerns; the traditional Healthcare imaging system, with its uneven Healthcare imaging resources, high Healthcare imaging costs, and diagnoses often relying on doctors’ clinical experience and equipment level limitations, has affected people’s demand for health, so there is a need for a more accurate, convenient, and affordable Healthcare imaging system that allows all people to enjoy fair and quality Healthcare imaging services. This paper discusses the construction and evaluation of an intelligent medical diagnostic model based on integrated deep neural networks, which not only provides a systematic diagnostic analysis of the various symptoms input by the inquirer but also has higher accuracy and efficiency compared with traditional medical diagnostic models. The construction of this model provides a theoretical basis for integrating deep neural networks applied to medical neighborhoods with big data algorithms.
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An, Ying, Yang Liu, Xianlai Chen, and Yu Sheng. "TERTIAN: Clinical Endpoint Prediction in ICU via Time-Aware Transformer-Based Hierarchical Attention Network." Computational Intelligence and Neuroscience 2022 (December 16, 2022): 1–13. http://dx.doi.org/10.1155/2022/4207940.
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Accurately predicting the clinical endpoint in ICU based on the patient’s electronic medical records (EMRs) is essential for the timely treatment of critically ill patients and allocation of medical resources. However, the patient’s EMRs usually consist of a large amount of heterogeneous multivariate time series data such as laboratory tests and vital signs, which are produced irregularly. Most existing methods fail to effectively model the time irregularity inherent in longitudinal patient medical records and capture the interrelationships among different types of data. To tackle these limitations, we propose a novel time-aware transformer-based hierarchical attention network (TERTIAN) for clinical endpoint prediction. In this model, a time-aware transformer is introduced to learn the personalized irregular temporal patterns of medical events, and a hierarchical attention mechanism is deployed to get the accurate patient fusion representation by comprehensively mining the interactions and correlations among multiple types of medical data. We evaluate our model on the MIMIC-III dataset and MIMIC-IV dataset for the task of mortality prediction, and the results show that TERTIAN achieves higher performance than state-of-the-art approaches.
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Kim, Sungwook. "A New Two-Stage Bargaining Game Approach for Intra- and Inter-WBAN Management." Mobile Information Systems 2021 (November 27, 2021): 1–10. http://dx.doi.org/10.1155/2021/5798741.
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The Internet of Medical Things (IoMT) is an amalgamation of smart devices to operate the wireless body area network (WBAN) by using networking technologies. To reduce the burden on WBANs, they link to the mobile edge computing (MEC), on which captured medical data can be stored and analyzed. In this paper, we design a new control scheme to effectively share the limited computation and communication resources in the MEC-assisted WBAN (M-W) platform. Based on the bargaining game theory, our proposed scheme explores the mutual benefits of intra- and inter-WBAN interactions. To dynamically adapt the current system conditions, we shape each WBAN’s aspirations to reach a reciprocal consensus for different application services. Utilizing two control factors, we provide a unifying framework for the study of intra- and inter-WBAN bargaining problems to share the limited system resource. Based on the feasibility and real-time effectiveness, the main novelty of the proposed scheme is the ability to achieve a relevant tradeoff between efficiency and fairness through the interactive bargaining process. At last, the experimental results show that the proposed scheme achieves substantial performance improvements to the comparison schemes.
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Uddin, Md Nazim, Md Shafiur Rahman, Md Harun-Or-Rashid Khandaker, M. Al Mamun, S. M. Mannan, Jean Sack, and Christine Dennehy. "Use and Impact of HINARI: An Observation in Bangladesh with Special Reference to icddr,b." Journal of Information & Knowledge Management 16, no. 01 (March 2017): 1750003. http://dx.doi.org/10.1142/s0219649217500034.
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This paper analyses the impact of the use of electronic resources and Health InterNetwork Access to Research Initiative (HINARI) services for medical research libraries in Bangladesh, emphasising the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b). Purposeful use of e-resources, time and cost-saving benefits, research impact, and challenges of using HINARI are discussed. The basic study was conducted at icddr,b in January–February 2014, using a mixed methodology, combining qualitative and quantitative approaches, including a background literature review, usage data shared from the HINARI secretariat at the World Health Organization (WHO), questionnaires, personal observations, and interviews with staff members of icddr,b. Findings revealed that icddr,b is the heaviest user of HINARI (a major source of public health and medical e-resources) in Bangladesh, with demonstrable increases of health research journal articles after introducing HINARI in 2003.
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Kostagiolas, Petros, Nikolaos Korfiatis, Panos Kourouthanasis, and Georgios Alexias. "Work-related factors influencing doctors search behaviors and trust toward medical information resources." International Journal of Information Management 34, no. 2 (April 2014): 80–88. http://dx.doi.org/10.1016/j.ijinfomgt.2013.11.009.
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Overby, Casey, John Connolly, Christopher Chute, Joshua Denny, Robert Freimuth, Andrea Hartzler, Ingrid Holm, et al. "Practical considerations for implementing genomic information resources." Applied Clinical Informatics 07, no. 03 (July 2016): 870–82. http://dx.doi.org/10.4338/aci-2016-04-ra-0060.
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SummaryTo understand opinions and perceptions on the state of information resources specifically targeted to genomics, and approaches to delivery in clinical practice.We conducted a survey of genomic content use and its clinical delivery from representatives across eight institutions in the electronic Medical Records and Genomics (eMERGE) network and two institutions in the Clinical Sequencing Exploratory Research (CSER) consortium in 2014.Eleven responses representing distinct projects across ten sites showed heterogeneity in how content is being delivered, with provider-facing content primarily delivered via the electronic health record (EHR) (n=10), and paper/pamphlets as the leading mode for patient-facing content (n=9). There was general agreement (91%) that new content is needed for patients and providers specific to genomics, and that while aspects of this content could be shared across institutions there remain site-specific needs (73% in agreement).This work identifies a need for the improved access to and expansion of information resources to support genomic medicine, and opportunities for content developers and EHR vendors to partner with institutions to develop needed resources, and streamline their use – such as a central content site in multiple modalities while implementing approaches to allow for site-specific customization. Citation: Rasmussen LV, Overby CL, Connolly J, Chute CG, Denny JC, Freimuth RR, Hartzler AL, Holm IA, Manzi S, Pathak J, Peissig PL, Smith M, Williams MS, Shirts BH, Stoffel EM, Tarczy-Hornoch P, Rohrer Vitek CR, Wolf WA, Starren J. Practical considerations for implementing genomic information resources – experiences from eMERGE and CSER.
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Syed Abdul, Shabbir, Umashankar Upadhyay, Daniel Salcedo, and Che-Wei Lin. "Virtual reality enhancing medical education and practice: Brief communication." DIGITAL HEALTH 8 (January 2022): 205520762211439. http://dx.doi.org/10.1177/20552076221143948.
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The COVID-19 pandemic has become a major cause of rapid globalization and digitization of educational institutions, including medical education. The adaptation to digital technologies is the purpose of best education and training practices in the development of the academic medical curriculum. Virtual reality (VR) is embraced by the 3D environment and network resources which allow the expansion of VR from the entertainment industry to the education industry. This brief communication explains our understanding and the challenges in adopting VR technologies for medical training at an academic medical center. Advancement in VR technology assists medical institutes to strategize for the further development of medical training and education. There is a timely need for persistence to make the VR content accessible widely and open source. There is an urgent need for collaboration of medical institutes and technology industries on the development of education-related VR content and simulations.
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STEFANESCU, RADU, XAVIER PENNEC, and NICHOLAS AYACHE. "GRID-ENABLED NON-RIGID REGISTRATION OF MEDICAL IMAGES." Parallel Processing Letters 14, no. 02 (June 2004): 197–216. http://dx.doi.org/10.1142/s0129626404001830.
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Over recent years, non-rigid registration has become a major issue in medical imaging. It consists in recovering a dense point-to-point correspondence field between two images, and usually takes a long time. This is in contrast to the needs of a clinical environment, where usability and speed are major constraints, leading to the necessity of reducing the computation time from slightly less than an hour to just a few minutes. As financial pressure makes it hard for healthcare organizations to invest in expensive high-performance computing (HPC) solutions, cluster computing proves to be a convenient solution to our computation needs, offering a large processing power at a low cost. Among the fast and efficient non-rigid registration methods, we chose the demons algorithm for its simplicity and good performances. The parallel implementation decomposes the correspondence field into spatial blocks, each block being assigned to a node of the cluster. We obtained an acceleration of 11 by using 15 2GHz PC's connected through a 1GB/s Ethernet network and reduced the computation time from 40min to 3min30. In order to further optimize the costs and the maintenance load, we investigate in the second part the transparent use of shared computing resources, either through a graphic client or a Web one.
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Lakshmi, Udaya, and Rosa I. Arriaga. "Warm Solutions: Centering Nurse Contributions in Medical Making." Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (November 7, 2022): 1–25. http://dx.doi.org/10.1145/3555771.
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Making medical devices in healthcare settings engages practitioners in organizational innovation. Nurses improvise physical workarounds at the bedside in response to patient needs. Yet nurse-led problem-solving is rarely centralized in an emerging innovation ecosystem through medical making. We interviewed medical makers in six healthcare makerspaces to understand factors for nurse inclusion in the medical making ecosystem. Findings from 16 multi-stakeholder interviews with 6 facilitators and 10 nurses in the USA, reveal insights into nurse-led problem-solving with and without the use of physical prototyping (making) in formal innovation spaces with maker technologies. We report how a nurse's capacity for making is practice-driven to address in-patient discomfort, repair their own practice, and update standardized workflows. Most nurses iterate on low-tech solutions facing barriers to formal collaboration when they attempt to scale up. Their technical capabilities extend from innovation-centered resources (e.g., lab spaces, technologies), often with complete reliance on facilitators who have limited authority in the medical system. We contribute to themes around practice-based innovation, participation in technology design, and articulation work for collaborative innovation. From nurse makers' experiences, we discuss how nurse participation can be supported in healthcare technology design.
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Siddique, Ali, Muhammad Azhar Iqbal, Muhammad Aleem, and Jerry Chun-Wei Lin. "A high-performance, hardware-based deep learning system for disease diagnosis." PeerJ Computer Science 8 (July 19, 2022): e1034. http://dx.doi.org/10.7717/peerj-cs.1034.
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Modern deep learning schemes have shown human-level performance in the area of medical science. However, the implementation of deep learning algorithms on dedicated hardware remains a challenging task because modern algorithms and neuronal activation functions are generally not hardware-friendly and require a lot of resources. Recently, researchers have come up with some hardware-friendly activation functions that can yield high throughput and high accuracy at the same time. In this context, we propose a hardware-based neural network that can predict the presence of cancer in humans with 98.23% accuracy. This is done by making use of cost-efficient, highly accurate activation functions, Sqish and LogSQNL. Due to its inherently parallel components, the system can classify a given sample in just one clock cycle, i.e., 15.75 nanoseconds. Though this system is dedicated to cancer diagnosis, it can predict the presence of many other diseases such as those of the heart. This is because the system is reconfigurable and can be programmed to classify any sample into one of two classes. The proposed hardware system requires about 983 slice registers, 2,655 slice look-up tables, and only 1.1 kilobits of on-chip memory. The system can predict about 63.5 million cancer samples in a second and can perform about 20 giga-operations per second. The proposed system is about 5–16 times cheaper and at least four times speedier than other dedicated hardware systems using neural networks for classification tasks.
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Srivastava, Arpan, Sonakshi Jain, Ryan Miranda, Shruti Patil, Sharnil Pandya, and Ketan Kotecha. "Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease." PeerJ Computer Science 7 (February 11, 2021): e369. http://dx.doi.org/10.7717/peerj-cs.369.
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In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain’s challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it provides a helping hand to them in coping with more patients. Apart from critical health diseases such as cancer and diabetes, the impact of respiratory diseases is also gradually on the rise and is becoming life-threatening for society. The early diagnosis and immediate treatment are crucial in respiratory diseases, and hence the audio of the respiratory sounds is proving very beneficial along with chest X-rays. The presented research work aims to apply Convolutional Neural Network based deep learning methodologies to assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for Chronic Obstructive Pulmonary detection. In the conducted experiments, we have used a Librosa machine learning library features such as MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q) and Chroma CENS. The presented system could also interpret the severity of the disease identified, such as mild, moderate, or acute. The investigation results validate the success of the proposed deep learning approach. The system classification accuracy has been enhanced to an ICBHI score of 93%. Furthermore, in the conducted experiments, we have applied K-fold Cross-Validation with ten splits to optimize the performance of the presented deep learning approach.
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Kim, Jina, Daeun Lee, and Eunil Park. "Machine Learning for Mental Health in Social Media: Bibliometric Study." Journal of Medical Internet Research 23, no. 3 (March 8, 2021): e24870. http://dx.doi.org/10.2196/24870.
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Background Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention. Objective We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media. Methods Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described. Results We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated. Conclusions The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.
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Mouronte-López, Mary Luz. "Modeling the Public Transport Networks: A Study of Their Efficiency." Complexity 2021 (August 13, 2021): 1–19. http://dx.doi.org/10.1155/2021/3280777.
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The public transportation network (PTN) provides mobility and access to community resources, employment, medical care, infrastructures, and other resources in the city. This research studies the process of the formation of links among nodes in different real-world PTNs. We have found that this process may be appropriately explained by a generalized linear model (GLM) using local, global, and quasilocal similarity indexes as explanatory variables. In modeling, the response variable was described by a binomial probability density function, and the logit function was used as a link function. In the crossvalidation process, utilising a downsampling approach, both average accuracy and area under the receiver operating characteristic curve (AUC) metrics presented higher values than 0.99. The kappa parameter had magnitudes larger than 0.93 for most of the PTNs. In the final validation stage, recall and specificity metrics took the value 1. Accuracy and precision parameters were larger than 0.99 and 0.87, respectively, for the majority of PTNs. Only one of the PTNs required utilising a smoothed bootstrap approach in order to achieve better results. The similarity measures with the greatest influence on the model were determined. We also assessed the impact of link removal on the global efficiency of PTNs, considering several similarity indexes. Additionally, we find that most of the networks show low local and global efficiencies (≤0.20), as well as travel times with a relevant variability, exhibiting standard deviations larger than 790 seconds. Significant similarities exist between the cumulative probability distributions of the local efficiency in all PTNs. With respect to the centrality measures, the eigenvector centrality presented a strong correlation with the hub/authority centralities (>0.80), while the pagerank showed a moderate, high, or very high correlation with the degree in all PTNs, >0.50.
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Alsuhibany, Suliman A., Sayed Abdel-Khalek, Ali Algarni, Aisha Fayomi, Deepak Gupta, Vinay Kumar, and Romany F. Mansour. "Ensemble of Deep Learning Based Clinical Decision Support System for Chronic Kidney Disease Diagnosis in Medical Internet of Things Environment." Computational Intelligence and Neuroscience 2021 (December 27, 2021): 1–13. http://dx.doi.org/10.1155/2021/4931450.
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Recently, Internet of Things (IoT) and cloud computing environments become commonly employed in several healthcare applications by the integration of monitoring things such as sensors and medical gadgets for observing remote patients. For availing of improved healthcare services, the huge count of data generated by IoT gadgets from the medicinal field can be investigated in the CC environment rather than relying on limited processing and storage resources. At the same time, earlier identification of chronic kidney disease (CKD) becomes essential to reduce the mortality rate significantly. This study develops an ensemble of deep learning based clinical decision support systems (EDL-CDSS) for CKD diagnosis in the IoT environment. The goal of the EDL-CDSS technique is to detect and classify different stages of CKD using the medical data collected by IoT devices and benchmark repositories. In addition, the EDL-CDSS technique involves the design of Adaptive Synthetic (ADASYN) technique for outlier detection process. Moreover, an ensemble of three models, namely, deep belief network (DBN), kernel extreme learning machine (KELM), and convolutional neural network with gated recurrent unit (CNN-GRU), are performed. Finally, quasi-oppositional butterfly optimization algorithm (QOBOA) is used for the hyperparameter tuning of the DBN and CNN-GRU models. A wide range of simulations was carried out and the outcomes are studied in terms of distinct measures. A brief outcomes analysis highlighted the supremacy of the EDL-CDSS technique on exiting approaches.
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Oloko-Oba, Mustapha, and Serestina Viriri. "Ensemble of EfficientNets for the Diagnosis of Tuberculosis." Computational Intelligence and Neuroscience 2021 (December 14, 2021): 1–12. http://dx.doi.org/10.1155/2021/9790894.
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Tuberculosis (TB) remains a life-threatening disease and is one of the leading causes of mortality in developing regions due to poverty and inadequate medical resources. Tuberculosis is medicable, but it necessitates early diagnosis through reliable screening techniques. Chest X-ray is a recommended screening procedure for identifying pulmonary abnormalities. Still, this recommendation is not enough without experienced radiologists to interpret the screening results, which forms part of the problems in rural communities. Consequently, various computer-aided diagnostic systems have been developed for the automatic detection of tuberculosis. However, their sensitivity and accuracy are still significant challenges that require constant improvement due to the severity of the disease. Hence, this study explores the application of a leading state-of-the-art convolutional neural network (EfficientNets) model for the classification of tuberculosis. Precisely, five variants of EfficientNets were fine-tuned and implemented on two prominent and publicly available chest X-ray datasets (Montgomery and Shenzhen). The experiments performed show that EfficientNet-B4 achieved the best accuracy of 92.33% and 94.35% on both datasets. These results were then improved through Ensemble learning and reached 97.44%. The performance recorded in this study portrays the efficiency of fine-tuning EfficientNets on medical imaging classification through Ensemble.
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Hosny, Khalid M., Akram M. Mortda, Nabil A. Lashin, and Mostafa M. Fouda. "A New Method to Detect Splicing Image Forgery Using Convolutional Neural Network." Applied Sciences 13, no. 3 (January 18, 2023): 1272. http://dx.doi.org/10.3390/app13031272.
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Recently, digital images have been considered the primary key for many applications, such as forensics, medical diagnosis, and social networks. Image forgery detection is considered one of the most complex digital image applications. More profoundly, image splicing was investigated as one of the common types of image forgery. As a result, we proposed a convolutional neural network (CNN) model for detecting splicing forged images in real-time and with high accuracy, with a small number of parameters as compared with the recently published approaches. The presented model is a lightweight model with only four convolutional layers and four max-pooling layers, which is suitable for most environments that have limitations in their resources. A detailed comparison was conducted between the proposed model and the other investigated models. The sensitivity and specificity of the proposed model over CASIA 1.0, CASIA 2.0, and CUISDE datasets are determined. The proposed model achieved an accuracy of 99.1% in detecting forgery on the CASIA 1.0 dataset, 99.3% in detecting forgery on the CASIA 2.0 dataset, and 100% in detecting forgery on the CUISDE dataset. The proposed model achieved high accuracy, with a small number of parameters. Therefore, specialists can use the proposed approach as an automated tool for real-time forged image detection.
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Chaudhry, Nauman Riaz, Anastasia Anagnostou, and Simon J. E. Taylor. "A Workflow Architecture for Cloud-based Distributed Simulation." ACM Transactions on Modeling and Computer Simulation 32, no. 2 (April 30, 2022): 1–26. http://dx.doi.org/10.1145/3503510.
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Distributed Simulation has still to be adopted significantly by the wider simulation community. Reasons for this might be that distributed simulation applications are difficult to develop and access to multiple computing resources are required. Cloud computing offers low-cost on-demand computing resources. Developing applications that can use cloud computing can be also complex, particularly those that can run on different clouds. Cloud-based Distributed Simulation (CBDS) is potentially attractive, as it may solve the computing resources issue as well as other cloud benefits, such as convenient network access. However, as possibly shown by the lack of sustainable approaches in the literature, the combination of cloud and distributed simulation may be far too complex to develop a general approach. E-Infrastructures have emerged as large-scale distributed systems that support high-performance computing in various scientific fields. Workflow Management Systems (WMS) have been created to simplify the use of these e-Infrastructures. There are many examples of where both technologies have been extended to use cloud computing. This article therefore presents our investigation into the potential of using these technologies for CBDS in the above context and the WORkflow architecture for cLoud-based Distributed Simulation (WORLDS), our contribution to CBDS. We present an implementation of WORLDS using the CloudSME Simulation Platform that combines the WS-PGRADE/gUSE WMS with the CloudBroker Platform as a Service. The approach is demonstrated with a case study using an agent-based distributed simulation of an Emergency Medical Service in REPAST and the Portico HLA RTI on the Amazon EC2 cloud.
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Wang, Wei, Yutao Li, Ji Li, Peng Zhang, and Xin Wang. "Detecting COVID-19 in Chest X-Ray Images via MCFF-Net." Computational Intelligence and Neuroscience 2021 (June 18, 2021): 1–8. http://dx.doi.org/10.1155/2021/3604900.
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COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2). Due to the rapid spread of COVID-19 around the world, the number of COVID-19 cases continues to increase, and lots of countries are facing tremendous pressure on both public and medical resources. Although RT-PCR is the most widely used detection technology with COVID-19 detection, it still has some limitations, such as high cost, being time-consuming, and having low sensitivity. According to the characteristics of chest X-ray (CXR) images, we design the Parallel Channel Attention Feature Fusion Module (PCAF), as well as a new structure of convolutional neural network MCFF-Net proposed based on PCAF. In order to improve the recognition efficiency, the network adopts 3 classifiers: 1-FC, GAP-FC, and Conv1-GAP. The experimental results show that the overall accuracy of MCFF-Net66-Conv1-GAP model is 94.66% for 4-class classification. Simultaneously, the classification accuracy, precision, sensitivity, specificity, and F1-score of COVID-19 are 100%. MCFF-Net may not only assist clinicians in making appropriate decisions for COVID-19 diagnosis but also mitigate the lack of testing kits.
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Kessler, Steven, Dennis Schroeder, Sergej Korlakov, Vincent Hettlich, Sebastian Kalkhoff, Sobhan Moazemi, Artur Lichtenberg, Falko Schmid, and Hug Aubin. "Predicting readmission to the cardiovascular intensive care unit using recurrent neural networks." DIGITAL HEALTH 9 (January 2023): 205520762211495. http://dx.doi.org/10.1177/20552076221149529.
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If a patient can be discharged from an intensive care unit (ICU) is usually decided by the treating physicians based on their clinical experience. However, nowadays limited capacities and growing socioeconomic burden of our health systems increase the pressure to discharge patients as early as possible, which may lead to higher readmission rates and potentially fatal consequences for the patients. Therefore, here we present a long short-term memory-based deep learning model (LSTM) trained on time series data from Medical Information Mart for Intensive Care (MIMIC-III) dataset to assist physicians in making decisions if patients can be safely discharged from cardiovascular ICUs. To underline the strengths of our LSTM we compare its performance with a logistic regression model, a random forest, extra trees, a feedforward neural network and with an already known, more complex LSTM as well as an LSTM combined with a convolutional neural network. The results of our evaluation show that our LSTM outperforms most of the above models in terms of area under receiver operating characteristic curve. Moreover, our LSTM shows the best performance with respect to the area under precision-recall curve. The deep learning solution presented in this article can help physicians decide on patient discharge from the ICU. This may not only help to increase the quality of patient care, but may also help to reduce costs and to optimize ICU resources. Further, the presented LSTM-based approach may help to improve existing and develop new medical machine learning prediction models.
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Ševkušić, Milica, Eleni Toli, Katerina Lenaki, Kalliopi Kanavou, Electra Sifakaki, Biljana Kosanović, Ilias Papastamatiou, and Elli Papadopoulou. "Building National Open Science Cloud Initiatives (NOSCIs) in Southeast Europe: Supporting Research and Scholarly Communication." Publications 10, no. 4 (November 8, 2022): 42. http://dx.doi.org/10.3390/publications10040042.
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The Horizon 2020 project National Initiatives for Open Science in Europe—NI4OS Europe supports the development of the European Open Science Cloud (EOSC) by integrating 15 countries in Southeast Europe into the governance structure of this new pan-European research environment. Through a qualitative secondary analysis of the data collected during the project, the paper focuses on the main instrument developed by the project with the aim of enabling the integration of the partner countries in the EOSC—a network of national Open Science Cloud Initiatives (NOSCIs)—and explains how the concept of NOSCI and a wide range of related activities, tools, services, and resources foster research and open scholarly communication. The paper has three main sections: the first identifies challenges to scholarly communication in Southeast Europe, the second describes the methodology used to deal with these challenges revolving around the concept of NOSCI, whereas the third presents a set of indicators to track the change generated by project actions and discusses the impact of this methodology and project outputs in the area of scholarly communication.
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Amami, Rimah, Suleiman Ali Al Saif, Rim Amami, Hassan Ahmed Eleraky, Fatma Melouli, and Mariem Baazaoui. "The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images." MENDEL 28, no. 1 (June 30, 2022): 1–7. http://dx.doi.org/10.13164/mendel.2022.1.001.
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The new Coronavirus or simply Covid-19 causes an acute deadly disease. It has spread rapidly across the world, which has caused serious consequences for health professionals and researchers. This is due to many reasons including the lack of vaccine, shortage of testing kits and resources. Therefore, the main purpose of this study is to present an inexpensive alternative diagnostic tool for the detection of Covid-19 infection by using chest radiographs and Deep Convolutional Neural Network (DCNN) technique. In this paper, we have proposed a reliable and economical solution to detect COVID-19. This will be achieved by using X-rays of patients and an Incremental-DCNN (I-DCNN) based on ResNet-101 architecture. The datasets used in this study were collected from publicly available chest radiographs on medical repositories. The proposed I-DCNN method will help in diagnosing the positive Covid-19 patient by utilising three chest X-ray imagery groups, these will be: Covid-19, viral pneumonia, and healthy cases. Furthermore, the main contribution of this paper resides on the use of incremental learning in order to accommodate the detection system. This has high computational energy requirements, time consuming challenges, while working with large-scale and regularly evolving images. The incremental learning process will allow the recognition system to learn new datasets, while keeping the convolutional layers learned previously. The overall Covid-19 detection rate obtained using the proposed I-DCNN was of 98.70\% which undeniably can contribute effectively to the detection of COVID-19 infection.
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Fang, Lu, Caixia An, and Bin Yi. "Research on Detection and Early Warning Mechanism of Emergency Public Health Medical Education System Based on Internet of Things Technology." Computational Intelligence and Neuroscience 2022 (June 24, 2022): 1–10. http://dx.doi.org/10.1155/2022/3008206.
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Sudden public health and medical education events have tested the stability of society to a great extent. The government need to strengthen capacity building, make use of system dynamic supervision, warn public health events in advance, and minimize the impact scope and related harmfulness of events. This not only facilitates the rapid mobilization of resources by the later government but also facilitates the comprehensive and detailed deployment and arrangement of decision-makers. As we all know, the Internet of Things is used by all walks of life because of its outstanding advantages of low power consumption, low cost, and wide range. Therefore, this article takes the Internet of Things as the technical basis of the system. According to the actual demand and resource design, it includes two system functions: detection and early warning. The results show that: (1) considering the practical principle, the evaluation system interface found that the scores of font size and color style are all below 80%, which need to be optimized and adjusted; the overall interface basically meets the needs of users. (2) The throughput of the three methods is different. The CoAP-E has superior throughput. (3) With the increase in packet loss rate, the request success rate of the CoAP method decreases in a “drop” manner. The CoAP-E method in this article has the best performance. (4) When the packet loss rate is 25%, the network adaptability of this method is the strongest, and the retransmission rate is less than 18%; the CoAP method is as high as 35%. (5) When the number of concurrent requests is less than 2500, there is no obvious difference between the three methods; the optimal performance of the dynamic load balancing method is 10.1 s. (6) The system comprehensively considers various factors of five site selections. The highest comprehensive score of Final Site, 5 is 8.7, which can be used as the resettlement place of emergency rescue facilities. This article starts from the characteristics and needs of public emergencies, and the final set of the system runs well. It can quickly reflect public health emergencies and medical education events. Use the most effective system functions for risk control, and maximize the analysis, organization, and coordination of events. The follow-up optimization of system details needs to be studied.
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Zhan, Yuzhuo, Weimin Lei, and Yunchong Guan. "A QoE Evaluation Method for RT-HDMV Based on Multipath Relay Service." Symmetry 11, no. 9 (September 5, 2019): 1127. http://dx.doi.org/10.3390/sym11091127.
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Multipath diversity leads to a possible higher performance for real-time high definition video, especially for medical video transmission, which would improve the stability of multiple transmission paths in the symmetrical state, and avoid the potential losses of imaging information in the communication process. Most of the previous works are always based on the single-path end-to-end transmission, although the service had been demonstrated that it is unable to meet the rigorous demand for the RT-HDMV. In the paper, a multipath relay service based on the QoE (quality of experience) evaluation method is proposed for the RT-HDMV (real-time high definition medical video). The method eliminates several of the limitations in the existing methods for some conventional single-path transmission. It can fully utilize the finite network resources and transmission bandwidth to meet the users’ demands of the RT-HDMV to get a better score of the QoE. We use a four-stage framework to evaluate the QoE, which consists of constructing the multipath relay transmission for the RT-HDMV, calculating the weights of diversified QoS parameters in the multipath, designing the load distribution strategy by the mapping between the QoS (quality of service) and QoE, and redefining the rule of the QoE evaluation. Many experiments show that the proposed design scheme achieves weighting of the transmission sub-paths and computes the QoE score. Compared with the state-of-art methods in the single path transmission scene, our framework mainly gains the excellent performance for the RT-HDMV.
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Amirgaliyev, Yedilkhan, Aliya Kalizhanova, Ainur Kozbakova, Zhalau Aitkulov, and Aygerim Astanayeva. "Development of a systematic approach and mathematical support for the evacuation process." Eastern-European Journal of Enterprise Technologies 3, no. 4 (111) (June 29, 2021): 31–42. http://dx.doi.org/10.15587/1729-4061.2021.234959.
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In modern conditions, due to the vastness of the territory of Kazakhstan, with a certain probability, natural disasters such as earthquakes, floods, avalanches, as well as accidents, destruction of buildings, epidemics, release of chemical toxic substances at industrial enterprises, fires in educational and medical institutions are possible, which justifies the relevance of modern methods and technologies for solving the problem of evacuation. The peculiarity of this work lies in the formation of an integrated approach for organizing the evacuation process both in peacetime as training for the event of an emergency situation (emergency), and in the event of the emergency itself. A conceptual diagram of an evacuation system is proposed that uses heterogeneous sources for receiving and transmitting information about the onset of an emergency. The input and output sources for receiving and transmitting information about the number of people in the building are determined. The main purpose of the system is to form an operational real-time evacuation plan. This work is the result of a phased implementation of an integrated evacuation system, which consists in building a mathematical model and a method for solving the problem of maximum flow in the network. A mathematical model has been developed for the optimal flow distribution along the Grindshiels network with the analysis of the flow formation and the characteristics of people’s motion in enclosed spaces. A game-theoretic approach and mathematical methods of the theory of hydraulic networks for finding an equilibrium state in flow-distribution networks have been developed. An algorithm for solving the evacuation problem using the graph approach is proposed. The results of this paper make it possible to systematically organize training evacuations, prepare resources, train the personnel responsible for evacuation in order to quickly respond in an emergency and carry out the evacuation process in order to avoid major consequences.
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Jain, Anshul, Tanya Singh, and Satyendra Kumar Sharma. "Security as a Solution: An Intrusion Detection System Using a Neural Network for IoT Enabled Healthcare Ecosystem." Interdisciplinary Journal of Information, Knowledge, and Management 16 (2021): 331–69. http://dx.doi.org/10.28945/4838.
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Aim/Purpose: The primary purpose of this study is to provide a cost-effective and artificial intelligence enabled security solution for IoT enabled healthcare ecosystem. It helps to implement, improve, and add new attributes to healthcare services. The paper aims to develop a method based on an artificial neural network technique to predict suspicious devices based on bandwidth usage. Background: COVID has made it mandatory to make medical services available online to every remote place. However, services in the healthcare ecosystem require fast, uninterrupted facilities while securing the data flowing through them. The solution in this paper addresses both the security and uninterrupted services issue. This paper proposes a neural network based solution to detect and disable suspicious devices without interrupting critical and life-saving services. Methodology: This paper is an advancement on our previous research, where we performed manual knowledge-based intrusion detection. In this research, all the experiments were executed in the healthcare domain. The mobility pattern of the devices was divided into six parts, and each one is assigned a dedicated slice. The security module regularly monitored all the clients connected to slices, and machine learning was used to detect and disable the problematic or suspicious devices. We have used MATLAB’s neural network to train the dataset and automatically detect and disable suspicious devices. The different network architectures and different training algorithms (Levenberg–Marquardt and Bayesian Framework) in MATLAB software have attempted to achieve more precise values with different properties. Five iterations of training were executed and compared to get the best result of R=99971. We configured the application to handle the four most applicable use cases. We also performed an experimental application simulation for the assessment and validation of predictions. Contribution: This paper provides a security solution for the IoT enabled healthcare system. The architectures discussed suggest an end-to-end solution on the sliced network. Efficient use of artificial neural networks detects and block suspicious devices. Moreover, the solution can be modified, configured and deployed in many other ecosystems like home automation. Findings: This simulation is a subset of the more extensive simulation previously performed on the sliced network to enhance its security. This paper trained the data using a neural network to make the application intelligent and robust. This enhancement helps detect suspicious devices and isolate them before any harm is caused on the network. The solution works both for an intrusion detection and prevention system by detecting and blocking them from using network resources. The result concludes that using multiple hidden layers and a non-linear transfer function, logsig improved the learning and results. Recommendations for Practitioners: Everything from offices, schools, colleges, and e-consultation is currently happening remotely. It has caused extensive pressure on the network where the data flowing through it has increased multifold. Therefore, it becomes our joint responsibility to provide a cost-effective and sustainable security solution for IoT enabled healthcare services. Practitioners can efficiently use this affordable solution compared to the expensive security options available in the commercial market and deploy it over a sliced network. The solution can be implemented by NGOs and federal governments to provide secure and affordable healthcare monitoring services to patients in remote locations. Recommendation for Researchers: Research can take this solution to the next level by integrating artificial intelligence into all the modules. They can augment this solution by making it compatible with the federal government’s data privacy laws. Authentication and encryption modules can be integrated to enhance it further. Impact on Society: COVID has given massive exposure to the healthcare sector since last year. With everything online, data security and privacy is the next most significant concern. This research can be of great support to those working for the security of health care services. This paper provides “Security as a Solution”, which can enhance the security of an otherwise less secure ecosystem. The healthcare use cases discussed in this paper address the most common security issues in the IoT enabled healthcare ecosystem. Future Research: We can enhance this application by including data privacy modules like authentication and authorisation, data encryption and help to abide by the federal privacy laws. In addition, machine learning and artificial intelligence can be extended to other modules of this application. Moreover, this experiment can be easily applicable to many other domains like e-homes, e-offices and many others. For example, e-homes can have devices like kitchen equipment, rooms, dining, cars, bicycles, and smartwatches. Therefore, one can use this application to monitor these devices and detect any suspicious activity.
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Shafiekhani, Sajad, Peyman Namdar, and Sima Rafiei. "A COVID-19 forecasting system for hospital needs using ANFIS and LSTM models: A graphical user interface unit." DIGITAL HEALTH 8 (January 2022): 205520762210850. http://dx.doi.org/10.1177/20552076221085057.
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Background Centers for Disease Control and Prevention data showed that about 40% of coronavirus disease 2019 (COVID-19) patients had been suffering from at least one underlying medical condition were hospitalized; in which nearly 33% of them needed to be admitted to the intensive care unit (ICU) to receive specialized medical services. Our study aimed to find a proper machine learning algorithm that can predict confirmed COVID-19 hospital admissions with high accuracy. Methods We obtained data on daily COVID-19 cases in regular medical inpatient units, emergency department, and ICU in the time window between 21 July 2020 and 21 November 2021. Data for the first 183 days (training data set) were used for long short-term memory (LSTM) network, adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and decision tree model training, whilst the remaining data for the last 60 days (test data set) were used for model validation. To predict the number of ICU and non-ICU patients, we used these models. Finally, a user-friendly graphical user interface unit was designed to load any time series data (here the trend of population of COVID-19 patients) and train LSTM, ANFIS, SVR or tree models for the prediction of COVID-19 cases for one week ahead. Results All models predicted the dynamics of COVID-19 cases in ICU and non- wards. The values of root-mean-square error and R2 as model assessment metrics showed that ANFIS model had better predictive power among all models. Conclusion Artificial intelligence-based forecasting models such as ANFIS system or deep learning approach based on LSTM or regression models including SVR or tree regression play a key role in forecasting the required number of beds or other types of medical facilities during the coronavirus pandemic. Thus, the designed graphical user interface of the present study can be used for optimum management of resources by health care systems amid COVID-19 pandemic.
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Voyne-Thrall, Michael. "Network Resources for Computer Music (Editor's Note in "Computer Music Journal" 18:1)." Computer Music Journal 18, no. 3 (1994): 8. http://dx.doi.org/10.2307/3681174.
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Behera, Ranjit Kumar, Amrut Patro, K. Hemant Kumar Reddy, and Diptendu Sinha Roy. "An Efficient Fog Layer Task Scheduling Algorithm for Multi-Tiered IoT Healthcare Systems." International Journal of Reliable and Quality E-Healthcare 11, no. 4 (October 1, 2022): 1–11. http://dx.doi.org/10.4018/ijrqeh.308802.
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IoT-based healthcare systems are becoming popular due to the extreme benefits patients, families, physicians, hospitals, and insurance companies are getting. Cloud is used traditionally for almost every IoT application, but cloud located far away from the devices resulted in an uncertain latency in providing services. At this point, fog computing emerged as the best alternative to provide such real-time services to delay-sensitive IoT applications. However, with the surge of patients, fog's limited resources may fail to handle the explosive growth in requests requiring advanced monitoring-based prioritization of tasks to meet the QoS requirements. To this end, in this paper, a level monitoring task scheduling (LMTS) algorithm is proposed for healthcare applications in fog to provide an immediate response to the delay-sensitive tasks with minimum delay and network usage. The proposed algorithm has been simulated using the Cloudsim simulator, and the results obtained demonstrated the efficacy of the proposed model.
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Lagerwerf, Marinus J., Daniël M. Pelt, Willem Jan Palenstijn, and Kees Joost Batenburg. "A Computationally Efficient Reconstruction Algorithm for Circular Cone-Beam Computed Tomography Using Shallow Neural Networks." Journal of Imaging 6, no. 12 (December 8, 2020): 135. http://dx.doi.org/10.3390/jimaging6120135.
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Circular cone-beam (CCB) Computed Tomography (CT) has become an integral part of industrial quality control, materials science and medical imaging. The need to acquire and process each scan in a short time naturally leads to trade-offs between speed and reconstruction quality, creating a need for fast reconstruction algorithms capable of creating accurate reconstructions from limited data. In this paper, we introduce the Neural Network Feldkamp–Davis–Kress (NN-FDK) algorithm. This algorithm adds a machine learning component to the FDK algorithm to improve its reconstruction accuracy while maintaining its computational efficiency. Moreover, the NN-FDK algorithm is designed such that it has low training data requirements and is fast to train. This ensures that the proposed algorithm can be used to improve image quality in high-throughput CT scanning settings, where FDK is currently used to keep pace with the acquisition speed using readily available computational resources. We compare the NN-FDK algorithm to two standard CT reconstruction algorithms and to two popular deep neural networks trained to remove reconstruction artifacts from the 2D slices of an FDK reconstruction. We show that the NN-FDK reconstruction algorithm is substantially faster in computing a reconstruction than all the tested alternative methods except for the standard FDK algorithm and we show it can compute accurate CCB CT reconstructions in cases of high noise, a low number of projection angles or large cone angles. Moreover, we show that the training time of an NN-FDK network is orders of magnitude lower than the considered deep neural networks, with only a slight reduction in reconstruction accuracy.
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BENMALEK, Elmehdi, Jamal EL MHAMDI, Abdelilah JILBAB, and Atman JBARI. "A COUGH-BASED COVID-19 DETECTION SYSTEM USING PCA AND MACHINE LEARNING CLASSIFIERS." Applied Computer Science 18, no. 4 (December 19, 2022): 96–115. http://dx.doi.org/10.35784/acs-2022-31.
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In 2019, the whole world is facing a health emergency due to the emergence of the coronavirus (COVID-19). About 223 countries are affected by the coronavirus. Medical and health services face difficulties to manage the disease, which requires a significant amount of health system resources. Several artificial intelligence-based systems are designed to automatically detect COVID-19 for limiting the spread of the virus. Researchers have found that this virus has a major impact on voice production due to the respiratory system's dysfunction. In this paper, we investigate and analyze the effectiveness of cough analysis to accurately detect COVID-19. To do so, we performed binary classification, distinguishing positive COVID patients from healthy controls. The records are collected from the Coswara Dataset, a crowdsourcing project from the Indian Institute of Science (IIS). After data collection, we extracted the MFCC from the cough records. These acoustic features are mapped directly to the Decision Tree (DT), k-nearest neighbor (kNN) for k equals to 3, support vector machine (SVM), and deep neural network (DNN), or after a dimensionality reduction using principal component analysis (PCA), with 95 percent variance or 6 principal components. The 3NN classifier with all features has produced the best classification results. It detects COVID-19 patients with an accuracy of 97.48 percent, 96.96 percent f1-score, and 0.95 MCC. Suggesting that this method can accurately distinguish healthy controls and COVID-19 patients.
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Lena-Acebo, Francisco-Javier, Ana Pérez-Escoda, Rosa García-Ruiz, and Manuel Fandos-Igado. "Redes sociales y smartphones como recursos para la enseñanza: percepción del profesorado en España." Pixel-Bit, Revista de Medios y Educación, no. 66 (2023): 239–70. http://dx.doi.org/10.12795/pixelbit.96788.
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Los nuevos entornos de aprendizaje y las redes sociales han supuesto un paso adelante con su uso en el aula llegando, incluso, a mostrarse como un importante refuerzo para la educación durante una situación de excepcionalidad provocada por la pandemia mundial del COVID-19. Las acciones y percepciones de los docentes al respecto han sido fundamentales para una respuesta rápida en un confinamiento global en el que las tecnologías móviles han jugado un papel fundamental. Este estudio presenta una investigación descriptiva correlacional con dos objetivos principales: en primer lugar, conocer la accesibilidad y el uso autodirigido de las redes sociales y, en segundo lugar, describir la utilidad percibida en SMD (dispositivos móviles inteligentes) y SNS (sitios de redes sociales) para los profesores. Con un enfoque metodológico cuantitativo y cualitativo se analizaron las correlaciones entre variables establecidas en una muestra de 2.048 profesores españoles. Los resultados destacan, en primer lugar, la ausencia de diferencias en cuanto a edad y género, demostrando así su máxima penetración entre los docentes; en segundo lugar, mostrando la relación entre la frecuencia de uso y la percepción positiva hacia la pertinencia en la actividad pedagógica. Las conclusiones abordadas desde el enfoque cualitativo muestran cuestiones interesantes que apuntan a la falta de conocimiento, sentido de responsabilidad y riesgos asociados cuando los docentes expresan sus percepciones sobre el uso de SMD y SNS en una integración pedagógica. Esto implica una perspectiva positiva ya que los docentes demandan apoyo y capacitación, pero no muestran rechazo como cabría esperar.
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Karaivanova, Aneta, and Anastas Mishev. "Introduction to the Special Issue on E-Infrastructures for Excellent Science: Advances in Life Sciences, Digital Cultural Heritage and Climatology." Scalable Computing: Practice and Experience 19, no. 2 (May 10, 2018): iii—iv. http://dx.doi.org/10.12694/scpe.v19i2.1401.
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It is our pleasure to present this special issue of scientific journal Scalable Computing: Practice and Experience. In this issue (Volume 19, No 2 – June 2018), we selected 14 papers which have gone through review and revision, and represent novel results in Life Sciences, Digital Cultural Heritage, Climatology using state-of-the-art e-infrastructures. E-Infrastructures are currently addressing the challenging needs of researchers for digital services in terms of networking, computing and data management. Virtual research environments (VRE) integrate resources across all layers of the e-infrastructure (networking, computing, data, software, user interfaces) to foster cross-disciplinary data interoperability. VRE are supporting innovation in research via an integrated access to potentially unlimited digital research resources, tools and services across disciplines and user communities.
In the content of this special issue the papers are ordered in 4 groups: Climatology (5 papers), Life Sciences (3 papers), Digital Cultural Heritage (2 papers) and Tools and Services (4 papers). Inside the groups, the papers are ordered by alphabetical order (the family name of the first author).
Climatology. The first paper presents an online interactive platform that aims to provide weather information about Armenia by integrating observations, model and satellite data. The topic is interesting from the practical point of view and might be very useful, especially for meteorologists. The second paper studies the effect of the dust on climate in the Caucasus region, with a specific focus on Georgia, using the Regional Climate Model RegCM interactively coupled to a dust model. The simulations cover the period 1985-2014 encompassing most of the Sahara, the Middle East, the Great Caucasus with adjacent regions. The third paper provides insight in the performances of wind simulations for high resolution models of the terrain. The presented results rationalize the possibility to run in reasonable time high resolution models, while showing that the impact of turbulence does not have significantly increases the computing requirement. The fourth paper presents adaptation and tuning of the RegCM model for the Balkan Peninsula and Bulgaria and development of a methodology able to predict possible changes of the regional climate for different global climate change scenarios and their impact on spatial/temporal distribution of precipitation, hence the global water budgets, to changes of the characteristics and spatial/temporal distribution of extreme, unfavorable and catastrophic events. The fifth paper presents comparison of two approaches (static and dynamical) used to compute the vibrational spectra of two conformers of the free formic acid molecule. The topic is interesting within the context of the atmospheric chemistry research field and it is of sufficient importance regarding the vibrational spectroscopic data and induced temperature effects of intramolecular motions.
Life Sciences. The manuscript from Astsatryan et al. describes a platform, which consists of data repository and workflow management services for Molecular Dynamics simulations. The platform focuses on an interactive data visualization workflow service as a key to perform more in-depth analyzing of research data outputs.
The manuscript from Bigovic et al. describes the organic synthesis of three enol carbonate derivatives and the analysis of their interactions with T4 lysozyme L99A/M102Q using molecular dynamics (MD) simulations. The results obtained by different software packages are discussed. The manuscript from Koteska et al. describes a semi-empirical Molecular Dynamics study of irinotecan, a colon cancer drug, using the atom-centered density matrix propagation approach. The described methodology was used to study the structure, dynamics, and rovibrational spectrum of irinotecan.
DCH. The paper of Charalambous and Artopoulos presents the deployment of the Clowder CMS system and the development of extraction services to handle, manage and automatically process Digital Cultural Heritage data in order to enable virtual collaboration for research in the South East and Eastern Mediterranean region. Technical descriptions of the system are given and some results are provided. In the paper of Elfarargy and Rizq a software system called Virtual Museum Framework (VirMuF), which is a set of tools that can be used by non-developers to easily create and publish 3D virtual museums in a very short time is presented. VirMuF is an open-source and teams including software developers can further extend VirMuF to fit their needs.
Software Tools and Services. Dimitrov and Stoyanov present the Data Discovery Service supporting the VI-SEEM project Virtual Research Environment - VRE. The solution is based on an open source platform with special customization regarding the data harvesting methods from diverse data sources and updating the available content so that the users will seamlessly access all the data from a single point. The paper of Golubev et al. addresses the problems of optimization of medical image storing and secure access, using the DICOM system. Based on the Moldova DICOM Network architecture, the system enables distributed search, and transportation of DICOM images. Additionally, several optimization problems are addressed by the authors, along with the integration challenges within the VI-SEEM VRE. In the paper of Mishev et al. the design, requirements and implementation of a federated virtual research environment, based on the service orientation paradigm, offering anything as a service solutions, have been considered. The challenges of the service management implementation focusing on interoperability by design and service management standards have been discussed. The manuscript of Vudragovic et al. gives an extensive insight of the development and implementation of the DREAM dust model (DREAMCLIMATE service). Additionally, a use-case study of the premature mortality due to the desert dust in the North Africa - Europe - Middle East region for the 2005 obtained by the application of this model is presented, justifying the model and the applicability of the service itself.
We would like to thank all those who kindly contributed to this Special Issue: the authors who submitted their papers, reviewers for their kind help and cooperation, especially to Dr. Zoe Curnia, Dr. Theodoros Christoudias and Dr. George Artopoulos for their valuable remarks and suggestions. Our special gratitude is for the Editor-in-Chief, Professor Dana Petcu, for her constant support.
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Mavis, Brian E., and Joseph J. Brocato. "Virtual Discourse: Evaluating DR-ED as a Computer Mediated Communications Network for Medical Education." Journal of Educational Computing Research 19, no. 1 (July 1998): 53–65. http://dx.doi.org/10.2190/ru63-ejb7-2dec-mtxy.
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Finding ways to link educators conducting research and educational activities is vitally important toward promoting scholarship, resource sharing, and collaboration. One avenue for developing these linkages is a form of computer mediated communications technology called listservs. This article describes the development and evaluation of the DR-ED listserv. Through a subscriber demographic analysis, content analysis of messages, and a subscriber survey, the DR-ED listserv has proven to be successful in terms of the diverse subscriber base it serves and the broad range of topics subscribers have addressed. Further, it serves as a forum to keep current on innovations in medical education and facilitates the formation of collaborative scholarly networks. By way of conclusion, suggestions are offered for those interested in developing listserv technology of their own and conducting research on listservs in general.
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Tran-Nguyen, Kevin, Caroline Berger, Roxanne Bennett, Michelle Wall, Suzanne N. Morin, and Fateme Rajabiyazdi. "Mobile App Prototype in Older Adults for Postfracture Acute Pain Management: User-Centered Design Approach." JMIR Aging 5, no. 4 (October 17, 2022): e37772. http://dx.doi.org/10.2196/37772.
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Background Postfracture acute pain is often inadequately managed in older adults. Mobile health (mHealth) technologies can offer opportunities for self-management of pain; however, insufficient apps exist for acute pain management after a fracture, and none are designed for an older adult population. Objective This study aims to design, develop, and evaluate an mHealth app prototype using a human-centered design approach to support older adults in the self-management of postfracture acute pain. Methods This study used a multidisciplinary and user-centered design approach. Overall, 7 stakeholders (ie, 1 clinician-researcher specialized in internal medicine, 2 user experience designers, 1 computer science researcher, 1 clinical research assistant researcher, and 2 pharmacists) from the project team, together with 355 external stakeholders, were involved throughout our user-centered development process that included surveys, requirement elicitation, participatory design workshops, mobile app design and development, mobile app content development, and usability testing. We completed this study in 3 phases. We analyzed data from prior surveys administered to 305 members of the Canadian Osteoporosis Patient Network and 34 health care professionals to identify requirements for designing a low-fidelity prototype. Next, we facilitated 4 participatory design workshops with 6 participants for feedback on content, presentation, and interaction with our proposed low-fidelity prototype. After analyzing the collected data using thematic analysis, we designed a medium-fidelity prototype. Finally, to evaluate our medium-fidelity prototype, we conducted usability tests with 10 participants. The results informed the design of our high-fidelity prototype. Throughout all the phases of this development study, we incorporated inputs from health professionals to ensure the accuracy and validity of the medical content in our prototypes. Results We identified 3 categories of functionalities necessary to include in the design of our initial low-fidelity prototype: the need for support resources, diary entries, and access to educational materials. We then conducted a thematic analysis of the data collected in the design workshops, which revealed 4 themes: feedback on the user interface design and usability, requests for additional functionalities, feedback on medical guides and educational materials, and suggestions for additional medical content. On the basis of these results, we designed a medium-fidelity prototype. All the participants in the usability evaluation tests found the medium-fidelity prototype useful and easy to use. On the basis of the feedback and difficulties experienced by participants, we adjusted our design in preparation for the high-fidelity prototype. Conclusions We designed, developed, and evaluated an mHealth app to support older adults in the self-management of pain after a fracture. The participants found our proposed prototype useful for managing acute pain and easy to interact with and navigate. Assessment of the clinical outcomes and long-term effects of our proposed mHealth app will be evaluated in the future.