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Radhakrishnan, Arun, and Gowthamarajan Kuppusamy. "Theoretical Formulation Strategies towards Neutralizing Inter-individual Variability Associated with Tacrolimus Immunosuppressant Therapy: A Case Study on Nextgeneration Personalized Medicine." Current Drug Metabolism 22, no. 12 (October 2021): 939–56. http://dx.doi.org/10.2174/1389200222666211015153317.
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: Individualizing drug therapy and attaining maximum benefits of a drug devoid of adverse reactions is the benefit of personalized medicine. One of the important factors contributing to inter-individual variability is genetic polymorphism. As of now, dose titration is the only followed golden standard for implementing personalized medicine. Converting the genotypic data into an optimized dose has become easier now due to technology development. However, for many drugs, finding an individualized dose may not be successful, which further leads to a trial and error approach. These dose titration strategies are generally followed at the clinical level, and so industrial involvement and further standardizations are not feasible. On the other side, technologically driven pharmaceutical industries have multiple smart drug delivery systems which are underutilized towards personalized medicine. Transdisciplinary research with drug delivery science can additionally support the personalization by converting the traditional concept of “dose titration towards personalization” with novel “dose-cum-dosage form modification towards next-generation personalized medicine”; the latter approach is useful to overcome gene-based inter-individual variability by either blocking, to downregulate, or bypassing the biological protein generated by the polymorphic gene. This article elaborates an advanced approach to implementing personalized medicine with the support of novel drug delivery systems. As a case study, we further reviewed the genetic polymorphisms associated with tacrolimus and customized novel drug delivery systems to overcome these challenges factored towards personalized medicine for better clinical outcomes, thereby paving a new strategy for implementing personalized medicine for all other drug candidates.
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Hizel, H. Candan. "Highly personalized reports for personalized drug selection by expert systems as clinical decision support." Personalized Medicine 14, no. 2 (March 2017): 93–97. http://dx.doi.org/10.2217/pme-2016-0083.
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Banjar, Haneen, David Adelson, Fred Brown, and Naeem Chaudhri. "Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System." BioMed Research International 2017 (2017): 1–21. http://dx.doi.org/10.1155/2017/3587309.
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The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient’s genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.
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Maojo, V., J. A. Mitchell, and L. J. Frey. "Section 7: Bioinformatics: Bioinformatics Linkage of Heterogeneous Clinical and Genomic Information in Support of Personalized Medicine." Yearbook of Medical Informatics 16, no. 01 (August 2007): 98–105. http://dx.doi.org/10.1055/s-0038-1638533.
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SummaryBiomedical Informatics as a whole faces a difficult epistemological task, since there is no foundation to explain the complexities of modeling clinical medicine and the many relationships between genotype, phenotype, and environment. This paper discusses current efforts to investigate such relationships, intended to lead to better diagnostic and therapeutic procedures and the development of treatments that could make personalized medicine a reality.To achieve this goal there are a number of issues to overcome. Primary are the rapidly growing numbers of heterogeneous data sources which must be integrated to support personalized medicine. Solutions involving the use of domain driven information models of heterogeneous data sources are described in conjunction with controlled ontologies and terminologies. A number of such applications are discussed.Researchers have realized that many dimensions of biology and medicine aim to understand and model the informational mechanisms that support more precise clinical diagnostic, prognostic and therapeutic procedures. As long as data grows exponentially, novel Biomedical Informatics approaches and tools are needed to manage the data. Although researchers are typically able to manage this information within specific, usually narrow contexts of clinical investigation, novel approaches for both training and clinical usage must be developed.After some preliminary overoptimistic expectations, it seems clear now that genetics alone cannot transform medicine. In order to achieve this, heterogeneous clinical and genomic data source must be integrated in scientifically meaningful and productive systems. This will include hypothesis-driven scientific research systems along with well understood information systems to support such research. These in turn will enable the faster advancement of personalized medicine.
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Flores Fonseca, Víctor Manuel, and Aldo Quelopana. "An Intelligent System Prototype to support and sharing diagnoses of maligned tumours, based on personalized medicine philosophy." Inteligencia Artificial 19, no. 58 (December 18, 2016): 17. http://dx.doi.org/10.4114/intartif.vol19iss58pp17-22.
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Circulatory systems diseases are one of the most important causes of death in Chilean population according to a report presented by the Chilean National Bureau of Statistics (INE). Undoubtedly, these sad numbers arise an opportunity to analyse ways to improve this situation. Personalized Medicine is a new approach used to adapt standard medical treatments to individual characteristics of patients. Currently, several kinds of personalized-medicine software applications are building using Artificial Intelligent techniques and supported by techniques as Cloud Computing and Big Data. This architecture provides complex and varied information access, such as clinical data, genome data, patients’ treatment or drugs information, among others. This document describes a proposal to produce a method for generating and sharing medical information, particularly of maligned tumors in Chile. The prototype will be developed within the framework of the personalized medicine.
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Walsh, Seán, Evelyn E. C. de Jong, Janna E. van Timmeren, Abdalla Ibrahim, Inge Compter, Jurgen Peerlings, Sebastian Sanduleanu, et al. "Decision Support Systems in Oncology." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–9. http://dx.doi.org/10.1200/cci.18.00001.
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Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708 . As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this impending change. However, to bring about precision medicine, a fundamental conundrum must be solved: Human cognitive capacity, typically constrained to five variables for decision making in the context of the increasing number of available biomarkers and therapeutic options, is a limiting factor to the realization of precision medicine. Given this level of complexity and the restriction of human decision making, current methods are untenable. A solution to this challenge is multifactorial decision support systems (DSSs), continuously learning artificial intelligence platforms that integrate all available data—clinical, imaging, biologic, genetic, cost—to produce validated predictive models. DSSs compare the personalized probable outcomes—toxicity, tumor control, quality of life, cost effectiveness—of various care pathway decisions to ensure optimal efficacy and economy. DSSs can be integrated into the workflows both strategically (at the multidisciplinary tumor board level to support treatment choice, eg, surgery or radiotherapy) and tactically (at the specialist level to support treatment technique, eg, prostate spacer or not). In some countries, the reimbursement of certain treatments, such as proton therapy, is already conditional on the basis that a DSS is used. DSSs have many stakeholders—clinicians, medical directors, medical insurers, patient advocacy groups—and are a natural consequence of big data in health care. Here, we provide an overview of DSSs, their challenges, opportunities, and capacity to improve clinical decision making, with an emphasis on the utility in oncology.
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Gaebel, Jan, Johannes Keller, Daniel Schneider, Adrian Lindenmeyer, Thomas Neumuth, and Stefan Franke. "The Digital Twin: Modular Model-Based Approach to Personalized Medicine." Current Directions in Biomedical Engineering 7, no. 2 (October 1, 2021): 223–26. http://dx.doi.org/10.1515/cdbme-2021-2057.
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Abstract To overcome obstacles and complexity of decision making in clinical oncology, we propose an integrated clinical decision support approach; the Digital Twin. We analyse the reasons for frustration in applying clinical decision support and provide a multi-levelled approach to implementing a flexible system to support and strengthen clinical decisions. Describing medical patterns and contexts with Resource Description Framework (RDF) allows for standardised way of connecting medical knowledge and processing modules. Having flexible web-based interfaces integrated a multitude of heterogeneous data processing systems to either make clinical data available altogether, or provide calculations and assessments. Transition of the Digital Twin to clinical practice promises effective assistance and safer clinical decisions.
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Leong, T. Y. "Toward Patient-Centered, Personalized and Personal Decision Support and Knowledge Management: A Survey." Yearbook of Medical Informatics 21, no. 01 (August 2012): 104–12. http://dx.doi.org/10.1055/s-0038-1639439.
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SummaryThis paper summarizes there cent trends and highlights the challenges and opportunities in decision support and knowledge management for patient-centered, personalized, and personal healthcare.The discussions are based on a broad survey of related references, focusing on the most recent publications. Major advances are examined in the areas of i) shared decision making paradigms, ii) continuity of care infrastructures and architectures, iii) human factors and system design approaches, iv) knowledge management innovations, and v) practical deployment and change considerations.Many important initiatives, projects, and plans with promising results have been identified. The common themes focus on supporting the individual patients who are playing an increasing central role in their own care decision processes. New collaborative decision making paradigms and information infrastructure sare required to ensure effective continuity of care. Human factors and usability are crucial for the successful development and deployment of the relevant systems, tools, and aids. Advances in personalized medicine can be achieved through integrating genomic, phenotypic and other biological, individual, and population level information, and gaining useful insights from building and analyzing biological and other models at multiple levels of abstraction. Therefore, new Information and Communication Technologies and evaluation approaches are needed to effectively manage the scale and complexity of biomedical and health information, and adapt to the changing nature of clinical decision support.Recent research in decision support andknowledge management combines heterogeneous information and personal data to provide cost-effective, calibrated, personalized support in shared decision making at the point of care. Current and emerging efforts concentrate on developing or extendingconventional paradigms, techniques, systems,and architectures for the newpredictive, preemptive, and participatory healthcare model for patient-centered, personalized medicine. There is also an increasing emphasis on managing complexity with changing care models, processes, and settings.
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Dopazo, Joaquín, Douglas Maya-Miles, Federico García, Nicola Lorusso, Miguel Ángel Calleja, María Jesús Pareja, José López-Miranda, et al. "Implementing Personalized Medicine in COVID-19 in Andalusia: An Opportunity to Transform the Healthcare System." Journal of Personalized Medicine 11, no. 6 (May 26, 2021): 475. http://dx.doi.org/10.3390/jpm11060475.
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The COVID-19 pandemic represents an unprecedented opportunity to exploit the advantages of personalized medicine for the prevention, diagnosis, treatment, surveillance and management of a new challenge in public health. COVID-19 infection is highly variable, ranging from asymptomatic infections to severe, life-threatening manifestations. Personalized medicine can play a key role in elucidating individual susceptibility to the infection as well as inter-individual variability in clinical course, prognosis and response to treatment. Integrating personalized medicine into clinical practice can also transform health care by enabling the design of preventive and therapeutic strategies tailored to individual profiles, improving the detection of outbreaks or defining transmission patterns at an increasingly local level. SARS-CoV2 genome sequencing, together with the assessment of specific patient genetic variants, will support clinical decision-makers and ultimately better ways to fight this disease. Additionally, it would facilitate a better stratification and selection of patients for clinical trials, thus increasing the likelihood of obtaining positive results. Lastly, defining a national strategy to implement in clinical practice all available tools of personalized medicine in COVID-19 could be challenging but linked to a positive transformation of the health care system. In this review, we provide an update of the achievements, promises, and challenges of personalized medicine in the fight against COVID-19 from susceptibility to natural history and response to therapy, as well as from surveillance to control measures and vaccination. We also discuss strategies to facilitate the adoption of this new paradigm for medical and public health measures during and after the pandemic in health care systems.
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Litinskaia, E. L. "Control and Decision-Making Support in a Personalized Insulin Therapy System." Proceedings of Universities. Electronics 26, no. 2 (April 2021): 162–71. http://dx.doi.org/10.24151/1561-5405-2021-26-2-162-171.
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Insulin therapy automation is an actual research line in the glycemic control of diabetes mellitus type 1 patients. Development of closed-loop systems and methods will allow blood glucose maintaining in the physiological range. The work proposes the personalized insulin therapy system considered as a closed-loop control system based on feedback and external disturbances compensation principles. Automatic feedback-based glycemic control includes proportional reg-ulation of basal insulin infusion rate in relation to optimized thresholds inside the target range. To achieve bidirectional glycemic regulation the author proposes model predictive control for calculation of not only optimal profile of bolus infusion but also recommended corrective dose of carbohydrates. Besides, the comparative analysis of trends in measured and predicted profiles of blood glucose allows detecting and compensation of its unpredicted deviations. In silico testing of developed algorithms on nine virtual adults for 72 hours shows an ability for glucose maintaining in the target range for whole system operation time.
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Lu, Yu, Yang Pian, Penghe Chen, Qinggang Meng, and Yunbo Cao. "RadarMath: An Intelligent Tutoring System for Math Education." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 16087–90. http://dx.doi.org/10.1609/aaai.v35i18.18020.
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We propose and implement a novel intelligent tutoring system, called RadarMath, to support intelligent and personalized learning for math education. The system provides the services including automatic grading and personalized learning guidance. Specifically, two automatic grading models are designed to accomplish the tasks for scoring the text-answer and formula-answer questions respectively. An education-oriented knowledge graph with the individual learner’s knowledge state is used as the key tool for guiding the personalized learning process. The system demonstrates how the relevant AI techniques could be applied in today's intelligent tutoring systems.
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van Wijk, Y., I. Halilaj, E. van Limbergen, S. Walsh, L. Lutgens, P. Lambin, and B. G. L. Vanneste. "Decision Support Systems in Prostate Cancer Treatment: An Overview." BioMed Research International 2019 (June 6, 2019): 1–10. http://dx.doi.org/10.1155/2019/4961768.
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Background. A multifactorial decision support system (mDSS) is a tool designed to improve the clinical decision-making process, while using clinical inputs for an individual patient to generate case-specific advice. The study provides an overview of the literature to analyze current available mDSS focused on prostate cancer (PCa), in order to better understand the availability of decision support tools as well as where the current literature is lacking. Methods. We performed a MEDLINE literature search in July 2018. We divided the included studies into different sections: diagnostic, which aids in detection or staging of PCa; treatment, supporting the decision between treatment modalities; and patient, which focusses on informing the patient. We manually screened and excluded studies that did not contain an mDSS concerning prostate cancer and study proposals. Results. Our search resulted in twelve diagnostic mDSS; six treatment mDSS; two patient mDSS; and eight papers that could improve mDSS. Conclusions. Diagnosis mDSS is well represented in the literature as well as treatment mDSS considering external-beam radiotherapy; however, there is a lack of mDSS for other treatment modalities. The development of patient decision aids is a new field of research, and few successes have been made for PCa patients. These tools can improve personalized medicine but need to overcome a number of difficulties to be successful and require more research.
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Watt, Stuart, Wei Jiao, Andrew M. K. Brown, Teresa Petrocelli, Ben Tran, Tong Zhang, Janet Dancey, Lillian L. Siu, Lincoln D. Stein, and Vincent Ferretti. "Designing a web application for personalized medicine trials." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): e13107-e13107. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.e13107.
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e13107 Background: Clinical genomics uses information from a patient's genome in clinical decision-making, an example of personalized medicine. The OICR/UHN Genomics Cohort Study is assessing the feasibility and developing standard operating procedures for clinical genomics in late stage cancer patients to enable larger trials. Tumor DNA from consenting patients is sequenced across 19 genes to identify actionable mutations and inform use of targeted therapeutic agents. Informatics systems are critical as the study involves 40 staff in 5 cancer centres, 2 laboratories, 3 genomics technologies, and spans screening, consent, obtaining/processing biopsy samples, genomic analysis, clinical laboratory verification, and reporting for decision-making. Methods: We used a process-centered method to develop a web system to manage study activities. Initially it tracked patients, samples, genomic results, decisions and reports across the cohort, monitored progress and sent reminders, working alongside an electronic data capture (EDC) system for the trial's clinical and genomic results. We later added a system to read, store, and analyze the genomics data, and a knowledge base of mutations’ tumor frequency (from the COSMIC database) annotated with clinical significance and drug sensitivity to generate reports for clinicians. Results: The web tracker proved highly adaptive. The design method allowed procedural refinements mid-study, including changes in sample preparation, sample sources, and differences in nomenclature across technologies. As the study procedures stabilized, the system provided deeper support for clinical decision making, enabling the generation of draft reports for verification by an expert panel prior to forwarding to the treating physician. The web tracker complemented the EDC system with its fixed modules for collection of clinical data and genomic results. Conclusions: The system effectively complemented clinical trial software. An agile development process enabled procedures to be refined as feasibility issues were found and resolved, and enabled flexible analysis of mutation data. Our design approach helped stabilize effective procedures for a clinical genomics service, and establish means to assess its performance.
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Shmanev, Timofey, Viktoriya Ul'yanickaya, and Oksana Pokrovskaya. ""Smart Railway Station" — Complex of Innovative Systems." Bulletin of scientific research results 2022, no. 4 (December 24, 2022): 150–59. http://dx.doi.org/10.20295/2223-9987-2022-4-150-159.
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Purpose: Automation of control systems for life support and service processes at infrastructure facilities of passenger complex, in the single informational space, through digital platforms. Methods: The work uses experimental-theoretical level methods: cause-and-effect relationships, algorithmization, flowcharts and others. Results: Practical recommendations for transition from manual (personalized) labor on railway station complexes to automated system (processes), related to passenger service, are given. Practical significance: Recommendations are given on the usage and digitization of automated control systems for life support processes of railway station complex. The proposed solution is based on the transformation of processes aimed at step-by-step automation of production, service and commercial activities of railway station complexes.
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Kuznetsov, P., P. Kakorina, and A. Almazov. "Medical decision support systems on the basis of artificial intelligence — strategy for the development of personalized medicine of the next stage." Terapevt (General Physician), no. 1 (January 1, 2020): 48–53. http://dx.doi.org/10.33920/med-12-2001-06.
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Prospects of creating decision support systems (DSS) in health care are substantiated in the article, the feasibility of mass implementation of DSS and the principle of their work on the basis of a medical and digital system for managing human capital are analyzed, examples of clinical DSS in Russia and abroad on the basis of 4P medicine are provided.
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Sacchi, L., G. Lanzola, N. Viani, and S. Quaglini. "Personalization and Patient Involvement in Decision Support Systems: Current Trends." Yearbook of Medical Informatics 24, no. 01 (August 2015): 106–18. http://dx.doi.org/10.15265/iy-2015-015.
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Summary Objectives: This survey aims at highlighting the latest trends (2012-2014) on the development, use, and evaluation of Information and Communication Technologies (ICT) based decision support systems (DSSs) in medicine, with a particular focus on patient-centered and personalized care. Methods: We considered papers published on scientific journals, by querying PubMed and Web of ScienceTM. Included studies focused on the implementation or evaluation of ICT-based tools used in clinical practice. A separate search was performed on computerized physician order entry systems (CPOEs), since they are increasingly embedding patient-tailored decision support. Results: We found 73 papers on DSSs (53 on specific ICT tools) and 72 papers on CPOEs. Although decision support through the delivery of recommendations is frequent (28/53 papers), our review highlighted also DSSs only based on efficient information presentation (25/53). Patient participation in making decisions is still limited (9/53), and mostly focused on risk communication. The most represented medical area is cancer (12%). Policy makers are beginning to be included among stakeholders (6/73), but integration with hospital information systems is still low. Concerning knowledge representation/management issues, we identified a trend towards building inference engines on top of standard data models. Most of the tools (57%) underwent a formal assessment study, even if half of them aimed at evaluating usability and not effectiveness. Conclusions: Overall, we have noticed interesting evolutions of medical DSSs to improve communication with the patient, consider the economic and organizational impact, and use standard models for knowledge representation. However, systems focusing on patient-centered care still do not seem to be available at large.
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Yadav, Neha, Aditya Yinaganti, Ayushi Mairal, Shefali Tripathi, Jagannath Jayaraj, Hariharan Chinnasamy, and Santosh Misra. "Biowastes as a source of extracting chitin and chitosan for biomedical applications." Reciklaza i odrzivi razvoj 13, no. 1 (2020): 23–48. http://dx.doi.org/10.5937/ror2001023y.
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Biomaterials are designed to interact with biological systems in aid to wound healing, regeneration of tissue, mechanical support, and drug delivery to eventually improve current therapeutic outcomes. The adoption of biomaterials is increasing constantly in health care practices by making it more biocompatible and non-toxic under physiological conditions. These adoptions have been associated with improvements in therapeutic outcomes across the population, however, the dosage of therapeutics needed to successfully treat a disease is generally different for each individual and relies a lot on experiences of consultant doctors. Many times, it leads to human errors in deciding on drug doses, un-fit implants and explants and eventually adverse effects or less positive effects. The personalized medicine and devices bring forth the idea that the medicine should be tailored for a patient based on various characteristics, such as gender, age, genetic makeup, and lifestyle. These personalized medicine approaches include type of drugs, activation methods, nanoassemblies, biomedical devices, etc. Among these approaches, personalized biomedical devices have become popular with the advent of 3D printing technologies, which can make customized implants for each patient with minimum price, limited time, and high accuracy. Personalized biomedicine also involves designing of drug to cater the need of an individual with minimum side effects. In this review an effort has been made to introduce different aspects of customized biomedical agents like therapeutic biomolecules, nanomedicine, implants, and explants. This comprehensive review of literature indicates that use of 3D printing technology in producing drug releasing, biodegradable personalized implants could be better therapeutic solution for a range of medical conditions.
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Serrano, Martín, Ahmed Elmisery, Mícheál Ó. Foghlú, Willie Donnelly, Cristiano Storni, and Mikael Fernström. "Pervasive Computing Support in the Transition towards Personalised Health Systems." International Journal of E-Health and Medical Communications 2, no. 3 (July 2011): 31–47. http://dx.doi.org/10.4018/jehmc.2011070102.
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This paper discusses pervasive computing work in the transition from traditional health care programs to personalised health systems (pHealth). A chronological guided transition survey is discussed to highlight trends in medicine describing their most recent developments about health care systems. Future trends in this interdisciplinary techno-medical area are described as research goals. Particularly, research and technological efforts concerning ICT’s and pervasive computing in healthcare and medical applications are presented to identify systems requirements supporting secure and reliable networks and services. The main objectives are to summarise both the pHealth systems requirements providing end-user applications and the necessary pervasive computing support to interconnect device-based health care applications and distributed information data systems in secure and reliable forms, highlighting the role pervasive computing plays in this process. A generic personalised healthcare scheme is introduced to provide guidance in the transition and can be used for multiple medical and health applications. An example is briefly introduced by using the generic scheme proposed.
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Regan, K., and P. R. O. Payne. "From Molecules to Patients: The Clinical Applications of Translational Bioinformatics." Yearbook of Medical Informatics 24, no. 01 (August 2015): 164–69. http://dx.doi.org/10.15265/iy-2015-005.
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Summary Objective: In order to realize the promise of personalized medicine, Translational Bioinformatics (TBI) research will need to continue to address implementation issues across the clinical spectrum. In this review, we aim to evaluate the expanding field of TBI towards clinical applications, and define common themes and current gaps in order to motivate future research. Methods: Here we present the state-of-the-art of clinical implementation of TBI-based tools and resources. Our thematic analyses of a targeted literature search of recent TBI-related articles ranged across topics in genomics, data management, hypothesis generation, molecular epidemiology, diagnostics, therapeutics and personalized medicine. Results: Open areas of clinically-relevant TBI research identified in this review include developing data standards and best practices, publicly available resources, integrative systems-level approaches, user-friendly tools for clinical support, cloud computing solutions, emerging technologies and means to address pressing legal, ethical and social issues. Conclusions: There is a need for further research bridging the gap from foundational TBI-based theories and methodologies to clinical implementation. We have organized the topic themes presented in this review into four conceptual foci – domain analyses, knowledge engineering, computational architectures and computation methods alongside three stages of knowledge development in order to orient future TBI efforts to accelerate the goals of personalized medicine.
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Najafi, Ali, Neda Emami, and Taha Samad-Soltani. "Integration of Genomics Data and Electronic Health Records Toward Personalized Medicine: A Targeted Review." Frontiers in Health Informatics 10, no. 1 (August 22, 2021): 86. http://dx.doi.org/10.30699/fhi.v10i1.299.
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Introduction: Integration of rapidly expanding high-throughput omics technologies and electronic health record (EHR) has created an unprecedented advantage in terms of acquiring routine healthcare data to accelerate genetic discovery. In this regard, EHR can also provide several important advantages to omics research if the integration challenges are well handled. The main purpose of the present study was to review available and published knowledge in the related literature and then to classify and discuss stakeholders’ requirements in this domain.Material and Methods: At first, a broad electronic search of all available literature in English was conducted on the topic through a search in the databases of Medline, Web of Science, Institute of Electrical and Electronics Engineers (IEEE), Scopus, and Cochrane. Then, stakeholders’ requirements were tabulated, and finally, a word cloud was generated and analyzed to achieve functional and non-functional cases.Results: A total of 81 articles were included in the given analysis. Integration requirements also consisted of nine functional cases including a uniform approach to the interpretation of genetic tests, standardized terminologies and ontologies, structured data entry as much as possible, an integrated online patient portal, multiple data source handling, machine-readable storing and reporting, research-oriented requirements, pharmacogenomics decision support capabilities, and phenotyping algorithms and knowledge base. Besides, there were three non-functional cases comprised of interoperability of multiple systems, ethical, legal, security factor, and big data computations.Conclusion: The main challenges in this way could also have semantic and technical themes. Therefore, system developers could guarantee the success of systems by overcoming the given challenges.
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A.Jabbar, M., Shirina Samreen, and Rajanikanth Aluvalu. "The Future of Health care: Machine Learning." International Journal of Engineering & Technology 7, no. 4.6 (September 25, 2018): 23. http://dx.doi.org/10.14419/ijet.v7i4.6.20226.
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Machine learning (ML) is a rising field. Machine learning is to find patterns automatically and reason about data.ML enables personalized care called precision medicine. Machine learning methods have made advances in healthcare domain. This paper discuss about application of machine learning in health care. Machine learning will change health care within a few years. In future ML and AI will transform health care, but quality ML and AI decision support systems (DSS) Should Require to address the problems faced by patients and physicians in effective diagnosis.
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Kalra, Gagan, Sudeshna Sil Kar, Duriye Damla Sevgi, Anant Madabhushi, Sunil K. Srivastava, and Justis P. Ehlers. "Quantitative Imaging Biomarkers in Age-Related Macular Degeneration and Diabetic Eye Disease: A Step Closer to Precision Medicine." Journal of Personalized Medicine 11, no. 11 (November 8, 2021): 1161. http://dx.doi.org/10.3390/jpm11111161.
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The management of retinal diseases relies heavily on digital imaging data, including optical coherence tomography (OCT) and fluorescein angiography (FA). Targeted feature extraction and the objective quantification of features provide important opportunities in biomarker discovery, disease burden assessment, and predicting treatment response. Additional important advantages include increased objectivity in interpretation, longitudinal tracking, and ability to incorporate computational models to create automated diagnostic and clinical decision support systems. Advances in computational technology, including deep learning and radiomics, open new doors for developing an imaging phenotype that may provide in-depth personalized disease characterization and enhance opportunities in precision medicine. In this review, we summarize current quantitative and radiomic imaging biomarkers described in the literature for age-related macular degeneration and diabetic eye disease using imaging modalities such as OCT, FA, and OCT angiography (OCTA). Various approaches used to identify and extract these biomarkers that utilize artificial intelligence and deep learning are also summarized in this review. These quantifiable biomarkers and automated approaches have unleashed new frontiers of personalized medicine where treatments are tailored, based on patient-specific longitudinally trackable biomarkers, and response monitoring can be achieved with a high degree of accuracy.
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Foran, David J., Wenjin Chen, Huiqi Chu, Evita Sadimin, Doreen Loh, Gregory Riedlinger, Lauri A. Goodell, et al. "Roadmap to a Comprehensive Clinical Data Warehouse for Precision Medicine Applications in Oncology." Cancer Informatics 16 (January 1, 2017): 117693511769434. http://dx.doi.org/10.1177/1176935117694349.
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Leading institutions throughout the country have established Precision Medicine programs to support personalized treatment of patients. A cornerstone for these programs is the establishment of enterprise-wide Clinical Data Warehouses. Working shoulder-to-shoulder, a team of physicians, systems biologists, engineers, and scientists at Rutgers Cancer Institute of New Jersey have designed, developed, and implemented the Warehouse with information originating from data sources, including Electronic Medical Records, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology and Pathology archives, and Next Generation Sequencing services. Innovative solutions were implemented to detect and extract unstructured clinical information that was embedded in paper/text documents, including synoptic pathology reports. Supporting important precision medicine use cases, the growing Warehouse enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information of patient tumors individually or as part of large cohorts to identify changes and patterns that may influence treatment decisions and potential outcomes.
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Koutkias, V., and J. Bouaud. "Computerized Clinical Decision Support: Contributions from 2014." Yearbook of Medical Informatics 24, no. 01 (August 2015): 119–24. http://dx.doi.org/10.15265/iy-2015-036.
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Summary Objective: To summarize recent research and propose a selection of best papers published in 2014 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook.Method: A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs) and computerized provider order entry systems in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was finally organized to conclude on the selection of best papers. Results: Among the 1,254 returned papers published in 2014, the full review process selected four best papers. The first one is an experimental contribution to a better understanding of unintended uses of CDSSs. The second paper describes the effective use of previously collected data to tailor and adapt a CDSS. The third paper presents an innovative application that uses pharmacogenomic information to support personalized medicine. The fourth paper reports on the long-term effect of the routine use of a CDSS for antibiotic therapy. Conclusions: As health information technologies spread more and more meaningfully, CDSSs are improving to answer users’ needs more accurately. The exploitation of previously collected data and the use of genomic data for decision support has started to materialize. However, more work is still needed to address issues related to the correct usage of such technologies, and to assess their effective impact in the long term.
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Tamposis, Ioannis, Ioannis Tsougos, Anastasios Karatzas, Katerina Vassiou, Marianna Vlychou, and Vasileios Tzortzis. "PCaGuard: A Software Platform to Support Optimal Management of Prostate Cancer." Applied Clinical Informatics 13, no. 01 (January 2022): 091–99. http://dx.doi.org/10.1055/s-0041-1741481.
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Abstract Background and Objective Prostate cancer (PCa) is a severe public health issue and the most common cancer worldwide in men. Early diagnosis can lead to early treatment and long-term survival. The addition of the multiparametric magnetic resonance imaging in combination with ultrasound (mpMRI-U/S fusion) biopsy to the existing diagnostic tools improved prostate cancer detection. Use of both tools gradually increases in every day urological practice. Furthermore, advances in the area of information technology and artificial intelligence have led to the development of software platforms able to support clinical diagnosis and decision-making using patient data from personalized medicine. Methods We investigated the current aspects of implementation, architecture, and design of a health care information system able to handle and store a large number of clinical examination data along with medical images, and produce a risk calculator in a seamless and secure manner complying with data security/accuracy and personal data protection directives and standards simultaneously. Furthermore, we took into account interoperability support and connectivity to legacy and other information management systems. The platform was implemented using open source, modern frameworks, and development tools. Results The application showed that software platforms supporting patient follow-up monitoring can be effective, productive, and of extreme value, while at the same time, aiding toward the betterment medicine clinical workflows. Furthermore, it removes access barriers and restrictions to specialized care, especially for rural areas, providing the exchange of medical images and patient data, among hospitals and physicians. Conclusion This platform handles data to estimate the risk of prostate cancer detection using current state-of-the-art in eHealth systems and services while fusing emerging multidisciplinary and intersectoral approaches. This work offers the research community an open architecture framework that encourages the broader adoption of more robust and comprehensive systems in standard clinical practice.
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Potamias, George, Kleanthi Lakiotaki, Theodora Katsila, Ming Ta Michael Lee, Stavros Topouzis, David N. Cooper, and George P. Patrinos. "Deciphering next-generation pharmacogenomics: an information technology perspective." Open Biology 4, no. 7 (July 2014): 140071. http://dx.doi.org/10.1098/rsob.140071.
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In the post-genomic era, the rapid evolution of high-throughput genotyping technologies and the increased pace of production of genetic research data are continually prompting the development of appropriate informatics tools, systems and databases as we attempt to cope with the flood of incoming genetic information. Alongside new technologies that serve to enhance data connectivity, emerging information systems should contribute to the creation of a powerful knowledge environment for genotype-to-phenotype information in the context of translational medicine. In the area of pharmacogenomics and personalized medicine, it has become evident that database applications providing important information on the occurrence and consequences of gene variants involved in pharmacokinetics, pharmacodynamics, drug efficacy and drug toxicity will become an integral tool for researchers and medical practitioners alike. At the same time, two fundamental issues are inextricably linked to current developments, namely data sharing and data protection. Here, we discuss high-throughput and next-generation sequencing technology and its impact on pharmacogenomics research. In addition, we present advances and challenges in the field of pharmacogenomics information systems which have in turn triggered the development of an integrated electronic ‘pharmacogenomics assistant’. The system is designed to provide personalized drug recommendations based on linked genotype-to-phenotype pharmacogenomics data, as well as to support biomedical researchers in the identification of pharmacogenomics-related gene variants. The provisioned services are tuned in the framework of a single-access pharmacogenomics portal.
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Bogomolov, Alexey. "Information Technologies of Digital Adaptive Medicine." Informatics and Automation 20, no. 5 (September 8, 2021): 1154–82. http://dx.doi.org/10.15622/20.5.6.
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The article provides a comprehensive description of information technologies of digital adaptive medicine. The emphasis is on the applicability to the development of specialized automated complexes, software models and systems for studying the adaptive capabilities of a person to environmental conditions. Requirements for information technologies to enhance these capabilities are formulated. The features of information technologies are reflected in relation to the implementation of applied systemic studies of life support, preservation of professional health and prolongation of human longevity.
Six basic concepts of adaptive medicine with an emphasis on the features of the mathematical support for information processing are characterized, priorities for improving information technologies used in these concepts are determined.
The information technologies used in the tasks of ensuring the professional performance of a person with an emphasis on the need to use adequate methods for diagnosing the state of a person at all stages of professional activity and the need to develop technologies for digital twins that adequately simulate the adaptation processes and reactions of the body in real conditions are considered.
The characteristics of information technologies for personalized monitoring of health risks are given, which make it possible to objectify the effects of physical factors of the conditions of activity and to implement individual and collective informing of personnel about environmental hazards.
The urgent need to standardize information processing methods in the development of information technologies for digital adaptive medicine in the interests of ensuring physiological adequacy and mathematical correctness of approaches to obtaining and processing information about a person's state is shown.
It is concluded that the priorities for improving information technologies of digital adaptive medicine are associated with the implementation of the achievements of the fourth industrial revolution, including the concept of sociocyberphysical systems.
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Baber, Ronny, and Michael Kiehntopf. "Automation in biobanking from a laboratory medicine perspective." Journal of Laboratory Medicine 43, no. 6 (December 18, 2019): 329–38. http://dx.doi.org/10.1515/labmed-2019-0151.
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Abstract Biobanks are important infrastructures to support clinical research and developments in personalized medicine. Although biobanking is not a new invention it has gained importance in the last few years due to increased quality requirements for biological samples in biomedical research and new high resolution Omics-technologies. Moreover, quality-assured collection, processing and storage of biological samples with defined pre-analytical history plays a key role for reproducibility in scientific research and paves the path towards precision medicine. Due to the increasing need for large numbers of samples, both in basic as well as in translational research, particular attention must be paid to sample acquisition and preparation in order to guarantee the highest possible sample quality. This can be achieved by following best practices or implementation and operation of specific biobank quality management systems that are compliant with the new DIN EN ISO 20387. Moreover, automation of critical process steps in biobanking can help to reach the highest quality standard and consistent sample quality. The following article will present and discuss currently available solutions for process automation in biobanking.
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Alterovitz, Gil, Jeremy Warner, Peijin Zhang, Yishen Chen, Mollie Ullman-Cullere, David Kreda, and Isaac S. Kohane. "SMART on FHIR Genomics: facilitating standardized clinico-genomic apps." Journal of the American Medical Informatics Association 22, no. 6 (July 21, 2015): 1173–78. http://dx.doi.org/10.1093/jamia/ocv045.
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Abstract Background Supporting clinical decision support for personalized medicine will require linking genome and phenome variants to a patient’s electronic health record (EHR), at times on a vast scale. Clinico-genomic data standards will be needed to unify how genomic variant data are accessed from different sequencing systems. Methods A specification for the basis of a clinic-genomic standard, building upon the current Health Level Seven International Fast Healthcare Interoperability Resources (FHIR®) standard, was developed. An FHIR application protocol interface (API) layer was attached to proprietary sequencing platforms and EHRs in order to expose gene variant data for presentation to the end-user. Three representative apps based on the SMART platform were built to test end-to-end feasibility, including integration of genomic and clinical data. Results Successful design, deployment, and use of the API was demonstrated and adopted by HL7 Clinical Genomics Workgroup. Feasibility was shown through development of three apps by various types of users with background levels and locations. Conclusion This prototyping work suggests that an entirely data (and web) standards-based approach could prove both effective and efficient for advancing personalized medicine.
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Frix, Anne-Noëlle, François Cousin, Turkey Refaee, Fabio Bottari, Akshayaa Vaidyanathan, Colin Desir, Wim Vos, et al. "Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians." Journal of Personalized Medicine 11, no. 7 (June 25, 2021): 602. http://dx.doi.org/10.3390/jpm11070602.
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Artificial intelligence (AI) has increasingly been serving the field of radiology over the last 50 years. As modern medicine is evolving towards precision medicine, offering personalized patient care and treatment, the requirement for robust imaging biomarkers has gradually increased. Radiomics, a specific method generating high-throughput extraction of a tremendous amount of quantitative imaging data using data-characterization algorithms, has shown great potential in individuating imaging biomarkers. Radiomic analysis can be implemented through the following two methods: hand-crafted radiomic features extraction or deep learning algorithm. Its application in lung diseases can be used in clinical decision support systems, regarding its ability to develop descriptive and predictive models in many respiratory pathologies. The aim of this article is to review the recent literature on the topic, and briefly summarize the interest of radiomics in chest Computed Tomography (CT) and its pertinence in the field of pulmonary diseases, from a clinician’s perspective.
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Lega, S., M. Bramuzzo, and M. C. Dubinsky. "Therapeutic Drug Monitoring in Pediatric IBD: Current Application and Future Perspectives." Current Medicinal Chemistry 25, no. 24 (July 4, 2018): 2840–54. http://dx.doi.org/10.2174/0929867324666170911163021.
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Background: as the paradigm for IBD management is evolving from symptom control to the more ambitious goal of complete deep remission, the concept of personalized medicine, as a mean to deliver individualized treatment with the best effectiveness and safety profile, is becoming paramount. Therapeutic drug monitoring (TDM) is an essential part of personalized medicine and its role in the management of IBD patients is rapidly expanding. <p> Objective: to review the current knowledge that poses the rationale for the use of TDM, and the present and future role of TDM-based approaches in the management of pediatric IBD. <p> Method: literature review. <p> Results: the concept of TDM has been introduced in the field of IBD along with thiopurines, over a decade ago, and evolved around anti-TNF therapies. TDM-based strategies proved to be costeffective in the management of patients with loss of response to biologics and, more recently, proactive TDM to optimize drug exposure has been shown to reduce treatment failure and drug adverse events. The role of TDM with new biologics and the usefulness of software-systems support tools to guide drug dosing are now under investigation. <p> Conclusion: Therapeutic drug monitoring has the potential to maximize the cost-benefit profile of therapies and is becoming an essential part of IBD management.
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Bhatt, Snehal, Sue Crimmin, Jeffrey Gross, Elizabeth Nixon, Maggie Truong, Michael Weglos, and Lorena Kallal. "Next-Generation Compound Delivery Platforms to Support Miniaturized Biology." SLAS TECHNOLOGY: Translating Life Sciences Innovation 24, no. 3 (February 6, 2019): 245–55. http://dx.doi.org/10.1177/2472630318820017.
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Recent advancements in science and engineering are revolutionizing our understanding of an individual’s disease, and with this knowledge we are gaining an increasingly sophisticated understanding of how discovery can be transformed to deliver personalized medicines. To reach this future state, we must reengineer our approach to enable the use of more relevant human cellular models earlier in the drug discovery process. Stem cells and primary human cells represent more disease-relevant models than immortalized cell lines; however, due to both availability and cost, their use is limited in lead generation activities. Miniaturization of cellular assays below microtiter plate volumes will enable the use of more relevant cells in screening, but this would require a change in how test molecules are introduced to the biology. With these shifting paradigms, Discovery Supply teams at GlaxoSmithKline (GSK) are modernizing our sample handling approaches. Various emerging technologies such as microarrays, nanowells, and microfluidic devices could bring fundamental changes in conventional sample handling support as we transition from microtiter plates to well-less platforms. The discussion here is exploratory in nature and reviews ongoing proof-of-concept experiments. Our ultimate goal is to industrialize the sample management platforms to support future miniaturized biological assay systems.
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Lavariega, Juan C., Roberto Garza, Lorena G. Gómez, Victor J. Lara-Diaz, and Manuel J. Silva-Cavazos. "EEMI - An Electronic Health Record for Pediatricians." International Journal of Healthcare Information Systems and Informatics 11, no. 3 (July 2016): 57–69. http://dx.doi.org/10.4018/ijhisi.2016070104.
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The use of paper health records and handwritten prescriptions are prone to preset errors of misunderstanding instructions or interpretations that derive in affecting patients' health. Electronic Health Records (EHR) systems are useful tools that among other functions can assists physicians' tasks such as finding recommended medicines, their contraindications, and dosage for a given diagnosis, filling prescriptions and support data sharing with other systems. This paper presents EEMI, a Children EHR focused on assisting pediatricians in their daily office practice. EEMI functionality keeps the relationships among diagnosis, treatment, and medications. EEMI also calculates dosages and automatically creates prescriptions which can be personalized by the physician. The system also validates patient allergies. This paper also presents the current use of EHRs in Mexico, the Mexican Norm (NOM-024-SSA3-2010), standards for the development of electronic medical records and its relationships with other standards for data exchange and data representation in the health area.
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Young, Alexandria N., Georgette Moyle-Heyrman, J. Julie Kim, and Joanna E. Burdette. "Microphysiologic systems in female reproductive biology." Experimental Biology and Medicine 242, no. 17 (March 8, 2017): 1690–700. http://dx.doi.org/10.1177/1535370217697386.
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Microphysiologic systems (MPS), including new organ-on-a-chip technologies, recapitulate tissue microenvironments by employing specially designed tissue or cell culturing techniques and microfluidic flow. Such systems are designed to incorporate physiologic factors that conventional 2D or even 3D systems cannot, such as the multicellular dynamics of a tissue–tissue interface or physical forces like fluid sheer stress. The female reproductive system is a series of interconnected organs that are necessary to produce eggs, support embryo development and female health, and impact the functioning of non-reproductive tissues throughout the body. Despite its importance, the human reproductive tract has received less attention than other organ systems, such as the liver and kidney, in terms of modeling with MPS. In this review, we discuss current gaps in the field and areas for technological advancement through the application of MPS. We explore current MPS research in female reproductive biology, including fertilization, pregnancy, and female reproductive tract diseases, with a focus on their clinical applications. Impact statement This review discusses existing microphysiologic systems technology that may be applied to study of the female reproductive tract, and those currently in development to specifically investigate gametes, fertilization, embryo development, pregnancy, and diseases of the female reproductive tract. We focus on the clinical applicability of these new technologies in fields such as assisted reproductive technologies, drug testing, disease diagnostics, and personalized medicine.
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Gao, Shurui, and Weidong Meng. "Development of a Personalized Recommendation System for E-Commerce Products for Distributed Storage Systems." Computational Intelligence and Neuroscience 2022 (June 20, 2022): 1–13. http://dx.doi.org/10.1155/2022/4752981.
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Because the distributed storage system is based on network technology, it can store data in multiple independent low-cost physical storage devices, and it is also suitable for large-capacity storage, so it has become more and more popular. Today, common applications of distributed storage systems include cloud storage services, data center storage services, and P2P storage services. Typical ones are GFS, HDFS, OceanStore, and Dynamo. Due to regional and economic differences, the development level of global e-commerce (b2c) is very inconsistent. b2c contains the following key tags: buying and selling, which is the core of the website platform. E-commerce provides business users with transparent information and high-quality cheap products. Logistics is the basic guarantee for customers to execute transactions, and it is also a strict indicator of the website platform. There will be many visits during the operation of the e-commerce system, and the number of users in the early stage will increase exponentially. A safe and efficient e-commerce system can provide users with one-stop transaction support and convenient transaction processes. The personalized recommendation system has formulated some rules for certain fields, based on these rules, and defined certain types of knowledge for certain items to meet the needs of certain users and use the defined reasoning rules to generate recommendation results.
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Zubair, Abdul Rasak, Emmanuel Sinmiloluwa Olu-Flourish, and Martins Obinna Nnaukwu. "Smart Home, Support At Old Age And Support For Persons With Disabilities: Speech Processing For Control Of Energy." International Journal of Advanced Networking and Applications 13, no. 05 (2022): 5134–42. http://dx.doi.org/10.35444/ijana.2022.13507.
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Generally, conventional home wiring system use simple latching switch that is being connected to the power supply for controlling electrical appliances such as fan, light, washing machine, air conditioner and television. The switch is usually located at the wall near the electrical appliance. This requires the user to move to the location of the switches to control the appliances. There is rapid increase in the number of people with special needs like the elderly and the disabled. Smart houses are considered a good alternative for the independent life of older persons and persons with disabilities. A smart home is a home that provides its residents the comfort, the convenience and the ease of operation of devices at all times, irrespective of where the resident actually is within the house. Smart Homes include devices that have automatic functions and systems that can be remotely controlled by the user. The primary objective of a smart house is to enhance comfort, energy saving, security for the residents and independent living of people at old age and people with disabilities. A low-cost prototype of a voice controlled smart home system controlling four devices by an Arduino microcontroller via a four-channel relay is presented. Voice control is one of the easiest methods to give input commands and is a more personalized form of control, since it can be adapted and customized to a particular speaker’s voice. Voice recognition is a computer software program embedded in a hardware device with the ability to decode the human voice. Most voice recognition systems require “training” (also called “enrolment”) where an individual speaker reads text or isolated vocabulary into the system. The system analyses the person’s specific voices and uses it to fine-tune the recognition of that person’s command. Upon successful recognition of the voice command, the microcontroller drives the corresponding load with the help of the relay circuit. Voice or Speech Processing has been applied successfully for the control of the supply of energy to home appliances.
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Chechelnitskaya, S. M., A. V. Baerbach, D. V. Zhuk, V. A. Nikulin, A. G. Rumyantsev, and Yu V. Saraikin. "PERSONALIZED PHYSICAL REHABILITATION OF CHILDREN WITH CANCER." Pediatria. Journal named after G.N. Speransky 100, no. 3 (May 28, 2021): 61–69. http://dx.doi.org/10.24110/0031-403x-2021-100-3-61-69.
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The aim of the research is to study the feasibility and effectiveness of partner medicine programs conducted in full-time and part-time mode in rehabilitation of children with cancer. Materials and methods of research: the article presents a program of physical rehabilitation of children treated for oncological diseases (OD), developed at the Russkoe Pole Medical and Rehabilitation Scientific Center, based on the personal approach and partnerships between the child's family and specialists. The personal approach is based on data of instrumental examination of the actual physical condition of the child and the predicted risks of late toxic complications: somatometry, assessment of energy costs and exercise tolerance, Biomechanical examination of the locomotor apparatus, functional diagnostics of the respiratory and cardiovascular systems. Based on the results, a personal physical rehabilitation program was developed. The process of physical rehabilitation was carried out in a cyclic mode: a hospital period for examination, development of a personal program and implementation training (2 weeks), an inter-hospital period of independent studies with remote support of a doctor and exercise therapy methodologists (from 6 to 12 months). The effectiveness of the developed model was assessed according to three criteria: satisfaction of parents with participation in the program (questionnaire), adherence to recommended physical activity (questionnaire), and assessment of basic mobility (Terrenkur test). The rehabilitation protocol was tested in 135 children aged 6–18 years with hemoblastosis, brain tumors, solid tumors, malignant tumors of bones and skeletal muscles: 61 boys (45,2%) and 74 girls (54,8%). The average age of the participants was 12,6±3,4 years. Results: participation in the program increased parents' confidence in their own ability to help their child with physical exercises at home and formed their willingness to continue the course at home. After discharge, 76% of families followed the recommendations for at least 2 months, 46% additionally applied to recommended organizations for adaptive exercise. Within a period of three months, all families who continue to practice independently have sought advice from exercise therapy methodologists. For three months of home exercises all children adhering to the recommendations have demonstrated an increase in basic mobility. Conclusion: the study confirmed the advisability and desirability for parents of patients to partner with a team of specialists.
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Fu, Rong, Mijuan Tian, and Qianjun Tang. "The Design of Personalized Education Resource Recommendation System under Big Data." Computational Intelligence and Neuroscience 2022 (June 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/1359730.
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With the advent of the Internet and the era of big data, education is increasingly dependent on data resources to support product and business innovation, and the lack of data resources has severely limited the areas involved. As a general information filtering method, personalized recommendation systems analyze the historical interaction data between users and items to build user interest models in an environment of “information overload”, allowing users to discover and recommend information that interests them. However, the explosive growth of information in the network makes users wander in the sea of information, and it is increasingly difficult to find the information they really need, i.e., information overload. This has given rise to personalized recommendation systems, which currently have more mature applications in industries such as e-commerce, music services, and movie services. To this end, this paper studies and implements a customized educational resource recommendation system that can handle big data. The results show that the values of different similarity calculations all fluctuate with the gradual increase of the number of nearest neighbors, and the algorithm in this paper is maximum at the number of neighbors around 60; then, it is inferred that applying the calculation method to the recommendation algorithm will improve the recommendation accuracy. Therefore, education uses the concept of big data to process the huge amount of education data and find some correlations and laws in education, so as to realize “teaching according to the material, teaching according to the material”.
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Hisham, Morsi, and Morsi Nasma. "Holistic cancer management as a model for the emergence of a personalized bio-psycho-socio-spiritual model of diseases, development and management." Annals of Psychiatry and Treatment 6, no. 1 (July 26, 2022): 013–16. http://dx.doi.org/10.17352/apt.000039.
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Psycho-social support lies at the core of Patient and Family-Centered Care (PFCC) that health care systems aim to transform. The objective is to comprehensively inform patients and families of their health issues, empower them to take charge of their illness, and participate in making choices about managing their health and wellbeing [1].
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Biondi Zoccai, Giuseppe, Roberto Carnevale, Sebastiano Sciarretta, and Giacomo Frati. "Electronic cigarette." European Heart Journal Supplements 22, Supplement_E (March 23, 2020): E25—E29. http://dx.doi.org/10.1093/eurheartj/suaa053.
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Abstract Despite significant efforts during the last decades, cigarette smoking still remains prevalent. Discouraging the use of all tobacco products, it is certainly the most effective mean to enhance public health, but complete prohibition is unlikely to succeed. The greatest challenge is the approach to chronic smokers, particularly those affected with cardiovascular conditions. To better support these patients during the difficult process leading to complete smoke cessation, it is important to characterize each patient from a clinical and psychological perspective, introducing the most reliable approaches to incentivize and support abstinence, such as varenicline and nicotine replacement therapy, thus providing a personalized recommendation. The recent introduction of electronic systems for nicotine release or tobacco heating (electronic cigarettes), offers an important challenge. These devices are reasonably considered as lower risk tools, thus providing a useful alternative which unable the patient a smoother transition toward smoking cessation, also presenting an array of choices among which a personalized selection could be made. This technology, though, should not be overemphasized, considering also its potential harmful effects, and certainly its use should be strongly discouraged in non-smokers, particularly at young age. This approach, cautious and pragmatic, aside from demonization or over-enthusiastic appraisal, could provide favourable results in the constant struggle against cigarette smoking.
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Kim, Jae-Kwon, Sun-Jung Lee, Sung-Hoo Hong, and In-Young Choi. "Machine-Learning-Based Digital Twin System for Predicting the Progression of Prostate Cancer." Applied Sciences 12, no. 16 (August 15, 2022): 8156. http://dx.doi.org/10.3390/app12168156.
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Clinical decision support systems (CDSSs) enable users to make decisions based on clinical data from electronic medical records, facilitating personalized precision medicine treatments. A digital twin (DT) approach enables the interoperability between physical and virtual environments through data analysis using machine learning (ML). By combining DT with the prostate cancer (PCa) process, it is possible to predict cancer prognosis. In this study, we propose a DT-based prediction model for clinical decision-making in the PCa process. Pathology and biochemical recurrence (BCR) were predicted with ML using data from a clinical data warehouse and the PCa process. The DT model was developed using data from 404 patients. The BCR prediction accuracy increased according to the amount of data used, and reached as high as 96.25% when all data were used. The proposed DT-based predictive model can help provide a clinical decision support system for PCa. Further, it can be used to improve medical processes, promote health, and reduce medical costs and problems.
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Reinhardt, Erich R. "Workflow Solutions with Healthcare IT." Yearbook of Medical Informatics 16, no. 01 (August 2007): XI—XIII. http://dx.doi.org/10.1055/s-0038-1638516.
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SummaryTo discuss trends in information technology (IT) for the future of healthcare.To report from the viewpoint of a global healthcare IT enterprise.Healthcare IT consists of far more than electronic storage of information and automation of existing manual processes. It is the linchpin in an effective care process. Systems are available today that coordinate the complex processes across healthcare enterprises – providing alerts and reminders that can help healthcare providers not only operate more effectively but protect patient safety. The next revolution in healthcare information technology – personalized, evidence-based medicine, with information technology at the hub – is on the horizon.Although the healthcare industry has lagged behind many other industries in the adoption of sophisticated IT systems, perhaps no other industry can benefit as much from its use. Medical informatics subject matter experts must continue to advocate and support IT adoption for both the effects of process improvement and cost containment and for its potential to impact care outcomes.
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Shutters, Shade. "Urban Science: Putting the “Smart” in Smart Cities." Urban Science 2, no. 4 (September 20, 2018): 94. http://dx.doi.org/10.3390/urbansci2040094.
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Increased use of sensors and social data collection methods have provided cites with unprecedented amounts of data. Yet, data alone is no guarantee that cities will make smarter decisions and many of what we call smart cities would be more accurately described as data-driven cities. Parallel advances in theory are needed to make sense of those novel data streams and computationally intensive decision support models are needed to guide decision makers through the avalanche of new data. Fortunately, extraordinary increases in computational ability and data availability in the last two decades have led to revolutionary advances in the simulation and modeling of complex systems. Techniques, such as agent-based modeling and systems dynamic modeling, have taken advantage of these advances to make major contributions to diverse disciplines such as personalized medicine, computational chemistry, social dynamics, or behavioral economics. Urban systems, with dynamic webs of interacting human, institutional, environmental, and physical systems, are particularly suited to the application of these advanced modeling and simulation techniques. Contributions to this special issue highlight the use of such techniques and are particularly timely as an emerging science of cities begins to crystallize.
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Visco, Valeria, Carolina Vitale, Antonella Rispoli, Carmine Izzo, Nicola Virtuoso, Germano Junior Ferruzzi, Mario Santopietro, et al. "Post-COVID-19 Syndrome: Involvement and Interactions between Respiratory, Cardiovascular and Nervous Systems." Journal of Clinical Medicine 11, no. 3 (January 20, 2022): 524. http://dx.doi.org/10.3390/jcm11030524.
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Though the acute effects of SARS-CoV-2 infection have been extensively reported, the long-term effects are less well described. Specifically, while clinicians endure to battle COVID-19, we also need to develop broad strategies to manage post-COVID-19 symptoms and encourage those affected to seek suitable care. This review addresses the possible involvement of the lung, heart and brain in post-viral syndromes and describes suggested management of post-COVID-19 syndrome. Post-COVID-19 respiratory manifestations comprise coughing and shortness of breath. Furthermore, arrhythmias, palpitations, hypotension, increased heart rate, venous thromboembolic diseases, myocarditis and acute heart failure are usual cardiovascular events. Among neurological manifestations, headache, peripheral neuropathy symptoms, memory issues, lack of concentration and sleep disorders are most commonly observed with varying frequencies. Finally, mental health issues affecting mental abilities and mood fluctuations, namely anxiety and depression, are frequently seen. Finally, long COVID is a complex syndrome with protracted heterogeneous symptoms, and patients who experience post-COVID-19 sequelae require personalized treatment as well as ongoing support.
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Shakhovska, Nataliya, Ivan Izonin, and Nataliia Melnykova. "The Hierarchical Classifier for COVID-19 Resistance Evaluation." Data 6, no. 1 (January 15, 2021): 6. http://dx.doi.org/10.3390/data6010006.
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Finding dependencies in the data requires the analysis of relations between dozens of parameters of the studied process and hundreds of possible sources of influence on this process. Dependencies are nondeterministic and therefore modeling requires the use of statistical methods for analyzing random processes. Part of the information is often hidden from observation or not monitored. That is why many difficulties have arisen in the process of analyzing the collected information. The paper aims to find frequent patterns and parameters affected by COVID-19. The novelty of the paper is hierarchical architecture comprises supervised and unsupervised methods. It allows the development of an ensemble of the methods based on k-means clustering and classification. The best classifiers from the ensemble are random forest with 500 trees and XGBoost. Classification for separated clusters gives us higher accuracy on 4% in comparison with dataset analysis. The proposed approach can be used also for personalized medicine decision support in other domains. The features selection allows us to analyze the following features with the highest impact on COVID-19: age, sex, blood group, had influenza.
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Hendriks, Mathijs P., Xander A. A. M. Verbeek, Thijs van Vegchel, Maurice J. C. van der Sangen, Luc J. A. Strobbe, Jos W. S. Merkus, Harmien M. Zonderland, Carolien H. Smorenburg, Agnes Jager, and Sabine Siesling. "Transformation of the National Breast Cancer Guideline Into Data-Driven Clinical Decision Trees." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–14. http://dx.doi.org/10.1200/cci.18.00150.
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PURPOSE The essence of guideline recommendations often is intertwined in large texts. This impedes clinical implementation and evaluation and delays timely modular revisions needed to deal with an ever-growing amount of knowledge and application of personalized medicine. The aim of this project was to model guideline recommendations as data-driven clinical decision trees (CDTs) that are clinically interpretable and suitable for implementation in decision support systems. METHODS All recommendations of the Dutch national breast cancer guideline for nonmetastatic breast cancer were translated into CDTs. CDTs were constructed by nodes, branches, and leaves that represent data items (patient and tumor characteristics [eg, T stage]), data item values (eg, T2 or less), and recommendations (eg, chemotherapy), respectively. For all data items, source of origin was identified (eg, pathology), and where applicable, data item values were defined on the basis of existing classification and coding systems (eg, TNM, Breast Imaging Reporting and Data System, Systematized Nomenclature of Medicine). All unique routes through all CDTs were counted to measure the degree of data-based personalization of recommendations. RESULTS In total, 60 CDTs were necessary to cover the whole guideline and were driven by 114 data items. Data items originated from pathology (49%), radiology (27%), clinical (12%), and multidisciplinary team (12%) reports. Of all data items, 101 (89%) could be classified by existing classification and coding systems. All 60 CDTs could be integrated in an interactive decision support app that contained 376 unique patient subpopulations. CONCLUSION By defining data items unambiguously and unequivocally and coding them to an international coding system, it was possible to present a complex guideline as systematically constructed modular data-driven CDTs that are clinically interpretable and accessible in a decision support app.
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Barnard-Kelly, Katharine D., and William H. Polonsky. "Development of a Novel Tool to Support Engagement With Continuous Glucose Monitoring Systems and Optimize Outcomes." Journal of Diabetes Science and Technology 14, no. 1 (May 21, 2019): 151–54. http://dx.doi.org/10.1177/1932296819848686.
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Background: Increasing numbers of people with diabetes, especially those with type 1 diabetes (T1D), are using continuous glucose monitoring (CGM) systems to support their diabetes self-management, yet even so only approximately 30% of individuals with T1D meet the American Diabetes Association HbA1c target of 58 mmol/mol (7.5%) for children and 53 mmol/mol (7.0%) for adults. We aimed to produce a useful tool for people with diabetes to improve personalized understanding of CGM. Method: A brief leaflet titled “Guidelines to Improve Glucose Control Using CGM” was developed for people with diabetes. Semistructured interviews were held with 12 adults with T1D, focusing on their views regarding the relevance, readability, and usability of the newly revised leaflet. Participants were specifically asked to share what they would find most useful in terms of information and advice provided as well as how to make use of that in the context of their own diabetes self-management. Data were analyzed thematically and used to inform revisions of the leaflet content. Results: Data highlighted information and advice needs as well as personalization in terms of own diabetes management. Conclusions: CGM systems are associated with improved medical and psychosocial outcomes, especially when used effectively to meet the individual needs of the user. Ensuring greater understanding of the individual’s expectations when first starting CGM, matching experience and skills to meet those expectations, and tailoring use to the individual needs of each person with diabetes (PWD) are all required to achieve widespread and consistent benefit.
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Prasser, Fabian, Oliver Kohlbacher, Ulrich Mansmann, Bernhard Bauer, and Klaus Kuhn. "Data Integration for Future Medicine (DIFUTURE)." Methods of Information in Medicine 57, S 01 (July 2018): e57-e65. http://dx.doi.org/10.3414/me17-02-0022.
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Summary Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Future medicine will be predictive, preventive, personalized, participatory and digital. Data and knowledge at comprehensive depth and breadth need to be available for research and at the point of care as a basis for targeted diagnosis and therapy. Data integration and data sharing will be essential to achieve these goals. For this purpose, the consortium Data Integration for Future Medicine (DIFUTURE) will establish Data Integration Centers (DICs) at university medical centers. Objectives: The infrastructure envisioned by DIFUTURE will provide researchers with cross-site access to data and support physicians by innovative views on integrated data as well as by decision support components for personalized treatments. The aim of our use cases is to show that this accelerates innovation, improves health care processes and results in tangible benefits for our patients. To realize our vision, numerous challenges have to be addressed. The objective of this article is to describe our concepts and solutions on the technical and the organizational level with a specific focus on data integration and sharing. Governance and Policies: Data sharing implies significant security and privacy challenges. Therefore, state-of-the-art data protection, modern IT security concepts and patient trust play a central role in our approach. We have established governance structures and policies safeguarding data use and sharing by technical and organizational measures providing highest levels of data protection. One of our central policies is that adequate methods of data sharing for each use case and project will be selected based on rigorous risk and threat analyses. Interdisciplinary groups have been installed in order to manage change. Architectural Framework and Methodology: The DIFUTURE Data Integration Centers will implement a three-step approach to integrating, harmonizing and sharing structured, unstructured and omics data as well as images from clinical and research environments. First, data is imported and technically harmonized using common data and interface standards (including various IHE profiles, DICOM and HL7 FHIR). Second, data is preprocessed, transformed, harmonized and enriched within a staging and working environment. Third, data is imported into common analytics platforms and data models (including i2b2 and tranSMART) and made accessible in a form compliant with the interoperability requirements defined on the national level. Secure data access and sharing will be implemented with innovative combinations of privacy-enhancing technologies (safe data, safe settings, safe outputs) and methods of distributed computing. Use Cases: From the perspective of health care and medical research, our approach is disease-oriented and use-case driven, i.e. following the needs of physicians and researchers and aiming at measurable benefits for our patients. We will work on early diagnosis, tailored therapies and therapy decision tools with focuses on neurology, oncology and further disease entities. Our early uses cases will serve as blueprints for the following ones, verifying that the infrastructure developed by DIFUTURE is able to support a variety of application scenarios. Discussion: Own previous work, the use of internationally successful open source systems and a state-of-the-art software architecture are cornerstones of our approach. In the conceptual phase of the initiative, we have already prototypically implemented and tested the most important components of our architecture.
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Shmanev, T. M., V. I. Ulyanitskaya, and O. D. Pokrovskaya. "“Smart railway station” — automation system of the railway station complex." Transport Technician: Education and Practice 3, no. 3 (September 28, 2022): 305–11. http://dx.doi.org/10.46684/2687-1033.2022.3.305-311.
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The automation of control systems for life support processes and the provision of services at the infrastructure facilities of the passenger complex is considered. Digitization of borders, technologies, technical, technological and organizational processes involved in the provision of services for passengers, in the operation of station complexes, will make it possible to effectively manage engineering networks, minimizing gaps in the infrastructure and processes of transport systems.Causal relationships, algorithmization, mapping, data analysis, block diagramming, modeling, etc. are used. The main projects for the digital transformation of passenger facilities are identified, examples of local automated control systems for the life support processes of the station complex are given.The issue of automating feedback from passengers has been worked out separately, at the station complex, thanks to the information and reference telephone service, issues of a sanitary nature are centrally resolved. The proposed developments will make it possible to justify the development of technological areas within the frame-work of the Digital Railway program and the gradual transition from manual (personalized) labor at the station complex to an automated system during the modernization, reconstruction or introduction of local digital objects related to passenger service. As a result of these activities, it is expected to achieve the maximum efficiency of life support processes and the processes of rendering services of vault complexes.
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Shegai, P. V., P. A. Shatalov, A. A. Zabolotneva, N. A. Falaleeva, S. A. Ivanov, and A. D. Kaprin. "Challenges Faced by Clinicians in the Personalized Treatment Planning: A Literature Review and the First Results of the Russian National Cancer Program." Critical Care Research and Practice 2021 (September 23, 2021): 1–7. http://dx.doi.org/10.1155/2021/6649771.
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Advances in cancer molecular profiling have enabled the development of more effective approaches to the diagnosis and personalized treatment of tumors. However, treatment planning has become more labor intensive, requiring hours or even days of clinician effort to optimize an individual patient case in a trial-and-error manner. Lessons learned from the world cancer programs provide insights into ways to develop approaches for the treatment strategy definition which can be introduced into clinical practice. This article highlights the variety of breakthroughs in patients’ cancer treatment and some challenges that this field faces now in Russia. In this report, we consider the key characteristics for planning an optimal clinical treatment regimen and which should be included in the algorithm of clinical decision support systems. We discuss the perspectives of implementing artificial intelligence-based systems in cancer treatment planning in Russia.