Articles de revues : « Heterogeneous health data »

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Li, Gang-Guo, et Zheng-Zhi Wang. « Incorporating heterogeneous biological data sources in clustering gene expression data ». Health 01, n^o 01 (2009) : 17–23. http://dx.doi.org/10.4236/health.2009.11004.

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Mohammed, Noman, Xiaoqian Jiang, Rui Chen, Benjamin C. M. Fung et Lucila Ohno-Machado. « Privacy-preserving heterogeneous health data sharing ». Journal of the American Medical Informatics Association 20, n^o 3 (13 décembre 2012) : 462–69. http://dx.doi.org/10.1136/amiajnl-2012-001027.

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Hadzi-Pavlovic, Dusan. « Correlations III : heterogeneous data ». Acta Neuropsychiatrica 19, n^o 3 (juin 2007) : 215–16. http://dx.doi.org/10.1111/j.1601-5215.2007.00219.x.

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Sunindyo, Wikan Danar, Thomas Moser, Dietmar Winkler et Stefan Biffl. « Analyzing OSS Project Health with Heterogeneous Data Sources ». International Journal of Open Source Software and Processes 3, n^o 4 (octobre 2011) : 1–23. http://dx.doi.org/10.4018/jossp.2011100101.

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Stakeholders in Open Source Software (OSS) projects need to determine whether a project is likely to sustain for a sufficient period of time in order to justify their investments into this project. In an OSS project context, there are typically several data sources and OSS processes relevant for determining project health indicators. However, even within one project these data sources often are technically and/or semantically heterogeneous, which makes data collection and analysis tedious and error prone. In this paper, the authors propose and evaluate a framework for OSS data analysis (FOSSDA), which enables the efficient collection, integration, and analysis of data from heterogeneous sources. Major results of the empirical studies are: (a) the framework is useful for integrating data from heterogeneous data sources effectively and (b) project health indicators based on integrated data analyses were found to be more accurate than analyses based on individual non-integrated data sources.

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Zhao, Jing, Panagiotis Papapetrou, Lars Asker et Henrik Boström. « Learning from heterogeneous temporal data in electronic health records ». Journal of Biomedical Informatics 65 (janvier 2017) : 105–19. http://dx.doi.org/10.1016/j.jbi.2016.11.006.

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Ganguly, Sukanta, Pavandeep Kataria, Radmila Juric, Atila Ertas et Murat M. Tanik. « Sharing Information and Data Across Heterogeneous e-Health Systems ». Telemedicine and e-Health 15, n^o 5 (juin 2009) : 454–64. http://dx.doi.org/10.1089/tmj.2008.0149.

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Monga, H. K., et T. B. Patrick. « Error estimation in linking heterogeneous data sources ». Health Informatics Journal 7, n^o 3-4 (septembre 2001) : 135–37. http://dx.doi.org/10.1177/146045820100700305.

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Bleischwitz, Sinja, Tristan Salomon Winkelmann, Yvonne Pfeifer, Martin Alexander Fischer, Niels Pfennigwerth, Jens André Hammerl, Ulrike Binsker et al. « Antimicrobial Resistance Surveillance : Data Harmonisation and Data Selection within Secondary Data Use ». Antibiotics 13, n^o 7 (16 juillet 2024) : 656. http://dx.doi.org/10.3390/antibiotics13070656.

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Resistance to last-resort antibiotics is a global threat to public health. Therefore, surveillance and monitoring systems for antimicrobial resistance should be established on a national and international scale. For the development of a One Health surveillance system, we collected exemplary data on carbapenem and colistin-resistant bacterial isolates from human, animal, food, and environmental sources. We pooled secondary data from routine screenings, hospital outbreak investigations, and studies on antimicrobial resistance. For a joint One Health evaluation, this study incorporates epidemiological metadata with phenotypic resistance information and molecular data on the isolate level. To harmonise the heterogeneous original information for the intended use, we developed a generic strategy. By defining and categorising variables, followed by plausibility checks, we created a catalogue for prospective data collections and applied it to our dataset, enabling us to perform preliminary descriptive statistical analyses. This study shows the complexity of data management using heterogeneous secondary data pools and gives an insight into the early stages of the development of an AMR surveillance programme using secondary data.

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Paragliola, Giovanni, et Patrizia Ribino. « Exploring heterogeneous data distribution issues in e-health federated systems ». Biomedical Signal Processing and Control 92 (juin 2024) : 106039. http://dx.doi.org/10.1016/j.bspc.2024.106039.

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Li, Ruohong, Honglang Wang et Wanzhu Tu. « Robust estimation of heterogeneous treatment effects using electronic health record data ». Statistics in Medicine 40, n^o 11 (19 mars 2021) : 2713–52. http://dx.doi.org/10.1002/sim.8926.

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Mavrogiorgou, Argyro, Athanasios Kiourtis, George Manias, Chrysostomos Symvoulidis et Dimosthenis Kyriazis. « Batch and Streaming Data Ingestion towards Creating Holistic Health Records ». Emerging Science Journal 7, n^o 2 (14 février 2023) : 339–53. http://dx.doi.org/10.28991/esj-2023-07-02-03.

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The healthcare sector has been moving toward Electronic Health Record (EHR) systems that produce enormous amounts of healthcare data due to the increased emphasis on getting the appropriate information to the right person, wherever they are, at any time. This highlights the need for a holistic approach to ingest, exploit, and manage these huge amounts of data for achieving better health management and promotion in general. This manuscript proposes such an approach, providing a mechanism allowing all health ecosystem entities to obtain actionable knowledge from heterogeneous data in a multimodal way. The mechanism includes diverse techniques for automatically ingesting healthcare-related information from heterogeneous sources that produce batch/streaming data, managing, fusing, and aggregating this data into new data structures (i.e., Holistic Health Records (HHRs)). The latter enable the aggregation of data coming from different sources, such as Internet of Medical Things (IoMT) devices, online/offline platforms, while to effectively construct the HHRs, the mechanism develops various data management techniques covering the overall data path, from data acquisition and cleaning to data integration, modelling, and interpretation. The mechanism has been evaluated upon different healthcare scenarios, ranging from hospital-retrieved data to patient platforms, combined with data obtained from IoMT devices, having produced useful insights towards its successful and wide adaptation in this domain. In order to implement a paradigm shift from heterogeneous and independent data sources, limited data exploitation, and health records, the mechanism has combined multidisciplinary technologies toward HHRs. Doi: 10.28991/ESJ-2023-07-02-03 Full Text: PDF

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Adhikari, Shishir. « Discovering Heterogeneous Causal Effects in Relational Data ». Proceedings of the AAAI Conference on Artificial Intelligence 38, n^o 21 (24 mars 2024) : 23373–74. http://dx.doi.org/10.1609/aaai.v38i21.30387.

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Causal inference in relational data should account for the non-IID nature of the data and the interference phenomenon, which occurs when a unit's outcome is influenced by the treatments or outcomes of others. Existing solutions to causal inference under interference consider either homogeneous influence from peers or specific heterogeneous influence contexts (e.g., local neighborhood structure). This thesis investigates causal reasoning in relational data and the automated discovery of heterogeneous causal effects under arbitrary heterogeneous peer influence contexts and effect modification.

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Liu, Shikang, Fatemeh Vahedian, David Hachen, Omar Lizardo, Christian Poellabauer, Aaron Striegel et Tijana Milenković. « Heterogeneous Network Approach to Predict Individuals’ Mental Health ». ACM Transactions on Knowledge Discovery from Data 15, n^o 2 (avril 2021) : 1–26. http://dx.doi.org/10.1145/3429446.

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Depression and anxiety are critical public health issues affecting millions of people around the world. To identify individuals who are vulnerable to depression and anxiety, predictive models have been built that typically utilize data from one source. Unlike these traditional models, in this study, we leverage a rich heterogeneous dataset from the University of Notre Dame’s NetHealth study that collected individuals’ (student participants’) social interaction data via smartphones, health-related behavioral data via wearables (Fitbit), and trait data from surveys. To integrate the different types of information, we model the NetHealth data as a heterogeneous information network (HIN). Then, we redefine the problem of predicting individuals’ mental health conditions (depression or anxiety) in a novel manner, as applying to our HIN a popular paradigm of a recommender system (RS), which is typically used to predict the preference that a person would give to an item (e.g., a movie or book). In our case, the items are the individuals’ different mental health states. We evaluate four state-of-the-art RS approaches. Also, we model the prediction of individuals’ mental health as another problem type—that of node classification (NC) in our HIN, evaluating in the process four node features under logistic regression as a proof-of-concept classifier. We find that our RS and NC network methods produce more accurate predictions than a logistic regression model using the same NetHealth data in the traditional non-network fashion as well as a random-approach. Also, we find that the best of the considered RS approaches outperforms all considered NC approaches. This is the first study to integrate smartphone, wearable sensor, and survey data in a HIN manner and use RS or NC on the HIN to predict individuals’ mental health conditions.

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Suzuki, K. « New platform of data analytics for mental health ». European Psychiatry 33, S1 (mars 2016) : S33. http://dx.doi.org/10.1016/j.eurpsy.2016.01.863.

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IntroductionMental disorder is a key public health challenge and a leading cause of disability-adjusted life years (DALYs) due to its high level of disability and mortality. Therefore, a slight improvement on mental care provision and management could generate solid benefits on relieving the social burden of mental diseases.ObjectivesThis paper presents a long-term vision of strategic collaboration between Fujitsu Laboratories, Fujitsu Spain, and Hospital Clinico San Carlos to generate value through predictive and preventive medicine improving healthcare outcomes for every clinical area, benefiting managers, clinicians, and patients.AimsThe aim is to enable a data analytic approach towards a value-based healthcare system via health informatics. The project generates knowledge from heterogeneous data sources to obtain patterns assisting clinical decision-making.MethodsThis project leverages a data analytic platform named HIKARI (“light” in Japanese) to deliver the “right” information, to the “right” people, at the “right” time. HIKARI consists of a data-driven and evidence-based Decision Support and Recommendation System (DSRS), facilitating identification of patterns in large-scale hospital and open data sets and linking data from different sources and types.ResultsUsing multiple, heterogeneous data sets, HIKARI detects correlations from retrospective data and would facilitate early intervention when signs and symptoms prompt immediate actions. HIKARI also analyses resource consumption patterns and suggests better resource allocation, using real-world data.ConclusionsWith the advance of ICT, especially data-intensive computing paradigm, approaches mixing individual risk assessment and environmental conditions become increasingly available. As a key tool, HIKARI DSRS can assist clinicians in the daily management of mental disorders.Disclosure of interestThe author has not supplied his declaration of competing interest.

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Wang, Miye, Sheyu Li, Tao Zheng, Nan Li, Qingke Shi, Xuejun Zhuo, Renxin Ding et Yong Huang. « Big Data Health Care Platform With Multisource Heterogeneous Data Integration and Massive High-Dimensional Data Governance for Large Hospitals : Design, Development, and Application ». JMIR Medical Informatics 10, n^o 4 (13 avril 2022) : e36481. http://dx.doi.org/10.2196/36481.

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Background With the advent of data-intensive science, a full integration of big data science and health care will bring a cross-field revolution to the medical community in China. The concept big data represents not only a technology but also a resource and a method. Big data are regarded as an important strategic resource both at the national level and at the medical institutional level, thus great importance has been attached to the construction of a big data platform for health care. Objective We aimed to develop and implement a big data platform for a large hospital, to overcome difficulties in integrating, calculating, storing, and governing multisource heterogeneous data in a standardized way, as well as to ensure health care data security. Methods The project to build a big data platform at West China Hospital of Sichuan University was launched in 2017. The West China Hospital of Sichuan University big data platform has extracted, integrated, and governed data from different departments and sections of the hospital since January 2008. A master–slave mode was implemented to realize the real-time integration of multisource heterogeneous massive data, and an environment that separates heterogeneous characteristic data storage and calculation processes was built. A business-based metadata model was improved for data quality control, and a standardized health care data governance system and scientific closed-loop data security ecology were established. Results After 3 years of design, development, and testing, the West China Hospital of Sichuan University big data platform was formally brought online in November 2020. It has formed a massive multidimensional data resource database, with more than 12.49 million patients, 75.67 million visits, and 8475 data variables. Along with hospital operations data, newly generated data are entered into the platform in real time. Since its launch, the platform has supported more than 20 major projects and provided data service, storage, and computing power support to many scientific teams, facilitating a shift in the data support model—from conventional manual extraction to self-service retrieval (which has reached 8561 retrievals per month). Conclusions The platform can combine operation systems data from all departments and sections in a hospital to form a massive high-dimensional high-quality health care database that allows electronic medical records to be used effectively and taps into the value of data to fully support clinical services, scientific research, and operations management. The West China Hospital of Sichuan University big data platform can successfully generate multisource heterogeneous data storage and computing power. By effectively governing massive multidimensional data gathered from multiple sources, the West China Hospital of Sichuan University big data platform provides highly available data assets and thus has a high application value in the health care field. The West China Hospital of Sichuan University big data platform facilitates simpler and more efficient utilization of electronic medical record data for real-world research.

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Ayush Ahluwalia, Aanchal Sharma et Manasi Sharma. « Health Informatics & ; Data Analytics as A Career Choice ». International Healthcare Research Journal 6, n^o 2 (31 mai 2022) : RV11—RV13. http://dx.doi.org/10.26440/0602.05536.

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Data analytics and informatics both have become essential for the success and reputation of healthcare organizations and with the current increasing demand for the same by such organisations, a career path seems to be full of success and blooming opportunities. Big data analytics in medicine and healthcare covers integration and analysis of large amount of complex heterogeneous data such as various -omic data (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenomics, diseasomics), biomedical data and electronic health records data.

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Hernandez-Boussard, Tina. « Abstract IA14 : Linking heterogeneous data to enable knowledge discovery in health care ». Cancer Epidemiology, Biomarkers & ; Prevention 29, n^o 9_Supplement (1 septembre 2020) : IA14. http://dx.doi.org/10.1158/1538-7755.modpop19-ia14.

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Abstract The vision of precision medicine relies on the linkage of large-scale clinical, molecular, environmental, and patient-generated datasets. Traditionally, diverse data streams have been analyzed independently, including the wealth of information captured in electronic health records (EHRs). However, to successfully leverage the volumes of data that can be used in health care, cross-modality integration is necessary. We have developed a clinical data warehouse for prostate cancer that integrates multiple data streams, from structured EHR data to imaging, state registries to patient-generated data, as well as the rich granular information contained in unstructured clinical narrative text. This rich, longitudinal dataset facilitates secondary data use and enhances observational research in oncology. We have developed advanced machine learning approaches to analyze these data. Our methods can accurately classify patients into clinical and pathologic stage groups and prognostic risk groups. These classifications can be used at point of care to guide optimal treatment pathways based on evidence-based guidelines (e.g., identify high-risk patients for whom a radionuclide bone scan is recommended). Furthermore, linking routinely collected patient surveys to EHRs reveals important differences in global physical and mental health between demographic and clinical subgroups. Giving clinicians visibility into these patient-reported outcomes can help personalize treatment pathways and may inform population health initiatives to support vulnerable groups. The granular health data we collect and link also provide population-based views into changes in treatment patterns and effects from policy changes, e.g., changes to PSA screening guidelines. The integration of diverse data streams presents unique technical, semantic, and ethical challenges; however, our work suggests that multimodal clinical data can significantly improve the performance of prediction algorithms and guide treatment decisions, powering knowledge discovery at the patient and population level. Citation Format: Tina Hernandez-Boussard. Linking heterogeneous data to enable knowledge discovery in health care [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr IA14.

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Taweel, A., S. Miles, B. C. Delaney et R. Bache. « An Eligibility Criteria Query Language for Heterogeneous Data Warehouses ». Methods of Information in Medicine 54, n^o 01 (2015) : 41–44. http://dx.doi.org/10.3414/me13-02-0027.

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SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.Objectives: The increasing availability of electronic clinical data provides great potential for finding eligible patients for clinical research. However, data heterogeneity makes it difficult for clinical researchers to interrogate sources consistently. Existing standard query languages are often not sufficient to query across diverse representations. Thus, a higher- level domain language is needed so that queries become data-representation agnostic. To this end, we define a clinician-readable computational language for querying whether patients meet eligibility criteria (ECs) from clinical trials. This language is capable of implementing the temporal semantics required by many ECs, and can be automatically evaluated on heterogeneous data sources.Methods: By reference to standards and examples of existing ECs, a clinician-readable query language was developed. Using a model-based approach, it was implemented to transform captured ECs into queries that interrogate heterogeneous data warehouses. The query language was evaluated on two types of data sources, each different in structure and content.Results: The query language abstracts the level of expressivity so that researchers construct their ECs with no prior knowledge of the data sources. It was evaluated on two types of semantically and structurally diverse data warehouses. This query language is now used to express ECs in the EHR4CR project. A survey shows that it was perceived by the majority of users to be useful, easy to understand and unambiguous.Discussion: An EC-specific language enables clinical researchers to express their ECs as a query such that the user is isolated from complexities of different heterogeneous clinical data sets. More generally, the approach demonstrates that a domain query language has potential for overcoming the problems of semantic interoperability and is applicable where the nature of the queries is well understood and the data is conceptually similar but in different representations.Conclusions: Our language provides a strong basis for use across different clinical domains for expressing ECs by overcoming the heterogeneous nature of electronic clinical data whilst maintaining semantic consistency. It is readily comprehensible by target users. This demonstrates that a domain query language can be both usable and interoperable.

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Nadkarni, P. M., L. Marenco, R. Chen, E. Skoufos, G. Shepherd et P. Miller. « Organization of Heterogeneous Scientific Data Using the EAV/CR Representation ». Journal of the American Medical Informatics Association 6, n^o 6 (1 novembre 1999) : 478–93. http://dx.doi.org/10.1136/jamia.1999.0060478.

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Abidi, Syed Sibte Raza, et Samina Raza Abidi. « Intelligent health data analytics : A convergence of artificial intelligence and big data ». Healthcare Management Forum 32, n^o 4 (22 mai 2019) : 178–82. http://dx.doi.org/10.1177/0840470419846134.

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Healthcare is a living system that generates a significant volume of heterogeneous data. As healthcare systems are pivoting to value-based systems, intelligent and interactive analysis of health data is gaining significance for health system management, especially for resource optimization whilst improving care quality and health outcomes. Health data analytics is being influenced by new concepts and intelligent methods emanating from artificial intelligence and big data. In this article, we contextualize health data and health data analytics in terms of the emerging trends of artificial intelligence and big data. We examine the nature of health data using the big data criterion to understand “how big” is health data. Next, we explain the working of artificial intelligence–based data analytics methods and discuss “what insights” can be derived from a broad spectrum of health data analytics methods to improve health system management, health outcomes, knowledge discovery, and healthcare innovation.

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Yearout, R., R. Barger, G. Yates et D. Lisnerski. « A methodology for appropriate testing when data are heterogeneous ». International Journal of Industrial Ergonomics 24, n^o 1 (avril 1999) : 129–34. http://dx.doi.org/10.1016/s0169-8141(98)00094-8.

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Handel, Benjamin, et Jonathan Kolstad. « Wearable Technologies and Health Behaviors : New Data and New Methods to Understand Population Health ». American Economic Review 107, n^o 5 (1 mai 2017) : 481–85. http://dx.doi.org/10.1257/aer.p20171085.

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We study a randomized control trial in a large employer population of access to “wearable” technologies and the associated planning and monitoring tools on improved health behaviors (sleep and exercise). Both ITT and IV estimates based on actual plan enrollment for the treatment group suggest statistically significant but economically small changes in behavior after three months. We then implement machine learning-based models to assess treatment effect heterogeneity. We find little evidence for heterogeneous treatment effects base on observables. We also present detailed data on sleep patterns underscoring the value of this new data source to researchers.

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Yu, Xue, Ziyi Liu, Yifan Sun et Wu Wang. « Clustered Federated Learning for Heterogeneous Data (Student Abstract) ». Proceedings of the AAAI Conference on Artificial Intelligence 37, n^o 13 (26 juin 2023) : 16378–79. http://dx.doi.org/10.1609/aaai.v37i13.27049.

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Federated Learning (FL) aims to achieve a global model via aggregating models from all devices. However, it can diverge when the data on the users’ devices are heterogeneous. To address this issue, we propose a novel clustered FL method (FPFC) based on a nonconvex pairwise fusion penalty. FPFC can automatically identify clusters without prior knowledge of the number of clusters and the set of devices in each cluster. Our method is implemented in parallel, updates only a subset of devices at each communication round, and allows each participating device to perform inexact computation. We also provide convergence guarantees of FPFC for general nonconvex losses. Experiment results demonstrate the advantages of FPFC over existing methods.

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Chalkou, K., F. Pellegrini et G. Salanti. « NM2 Using Randomized and Observational DATA to Predict Heterogeneous Treatment Effects ». Value in Health 23 (décembre 2020) : S406—S407. http://dx.doi.org/10.1016/j.jval.2020.08.057.

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Qi, Ren, Zehua Zhang, Jin Wu, Lijun Dou, Lei Xu et Yue Cheng. « A new method for handling heterogeneous data in bioinformatics ». Computers in Biology and Medicine 170 (mars 2024) : 107937. http://dx.doi.org/10.1016/j.compbiomed.2024.107937.

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Ma, Xuan, Xiaoshan Yang, Junyu Gao et Changsheng Xu. « Health Status Prediction with Local-Global Heterogeneous Behavior Graph ». ACM Transactions on Multimedia Computing, Communications, and Applications 17, n^o 4 (30 novembre 2021) : 1–21. http://dx.doi.org/10.1145/3457893.

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Health management is getting increasing attention all over the world. However, existing health management mainly relies on hospital examination and treatment, which are complicated and untimely. The emergence of mobile devices provides the possibility to manage people’s health status in a convenient and instant way. Estimation of health status can be achieved with various kinds of data streams continuously collected from wearable sensors. However, these data streams are multi-source and heterogeneous, containing complex temporal structures with local contextual and global temporal aspects, which makes the feature learning and data joint utilization challenging. We propose to model the behavior-related multi-source data streams with a local-global graph, which contains multiple local context sub-graphs to learn short-term local context information with heterogeneous graph neural networks and a global temporal sub-graph to learn long-term dependency with self-attention networks. Then health status is predicted based on the structure-aware representation learned from the local-global behavior graph. We take experiments on the StudentLife dataset, and extensive results demonstrate the effectiveness of our proposed model.

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Manias, George, Ainhoa Azqueta-Alzúaz, Athanasios Dalianis, Jacob Griffiths, Maritini Kalogerini, Konstantina Kostopoulou, Eleftheria Kouremenou et al. « Advanced Data Processing of Pancreatic Cancer Data Integrating Ontologies and Machine Learning Techniques to Create Holistic Health Records ». Sensors 24, n^o 6 (7 mars 2024) : 1739. http://dx.doi.org/10.3390/s24061739.

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The modern healthcare landscape is overwhelmed by data derived from heterogeneous IoT data sources and Electronic Health Record (EHR) systems. Based on the advancements in data science and Machine Learning (ML), an improved ability to integrate and process the so-called primary and secondary data fosters the provision of real-time and personalized decisions. In that direction, an innovative mechanism for processing and integrating health-related data is introduced in this article. It describes the details of the mechanism and its internal subcomponents and workflows, together with the results from its utilization, validation, and evaluation in a real-world scenario. It also highlights the potential derived from the integration of primary and secondary data into Holistic Health Records (HHRs) and from the utilization of advanced ML-based and Semantic Web techniques to improve the quality, reliability, and interoperability of the examined data. The viability of this approach is evaluated through heterogeneous healthcare datasets pertaining to personalized risk identification and monitoring related to pancreatic cancer. The key outcomes and innovations of this mechanism are the introduction of the HHRs, which facilitate the capturing of all health determinants in a harmonized way, and a holistic data ingestion mechanism for advanced data processing and analysis.

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Canuel, Vincent, Hector Countouris, Pierre Laurent-Puig, Anita Burgun et Bastien Rance. « Integrating Heterogeneous Biomedical Data for Cancer Research : the CARPEM infrastructure ». Applied Clinical Informatics 07, n^o 02 (avril 2016) : 260–74. http://dx.doi.org/10.4338/aci-2015-09-ra-0125.

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SummaryCancer research involves numerous disciplines. The multiplicity of data sources and their heterogeneous nature render the integration and the exploration of the data more and more complex. Translational research platforms are a promising way to assist scientists in these tasks. In this article, we identify a set of scientific and technical principles needed to build a translational research platform compatible with ethical requirements, data protection and data-integration problems. We describe the solution adopted by the CARPEM cancer research program to design and deploy a platform able to integrate retrospective, prospective, and day-to-day care data. We designed a three-layer architecture composed of a data collection layer, a data integration layer and a data access layer. We leverage a set of open-source resources including i2b2 and tranSMART.Citation: Rance B, Canuel V, Countouris H, Laurent-Puig P, Burgun A. Integrating heterogeneous biomedical data for cancer research: the CARPEM infrastructure.

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Ye, Linglong, Jiecheng Luo, Ben-Chang Shia et Ya Fang. « MULTIDIMENSIONALLY HETEROGENEOUS HEALTH LATENT CLASSES AND HEALTHCARE UTILIZATION FOR OLDER CHINESE ». Innovation in Aging 3, Supplement_1 (novembre 2019) : S690. http://dx.doi.org/10.1093/geroni/igz038.2542.

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Abstract Objectives: Based on a multidimensional perspective, this study aimed to assess the heterogeneous health latent classes of older Chinese, and further examined the effects of health latent classes and associated factors on healthcare utilization. Methods: Data came from the Chinese Longitudinal Healthy Longevity Survey in 2014. Latent class analysis was adopted to identify heterogeneous health latent classes by health indicators of physical, psychological, and social dimensions. Two-part models were used to evaluate the impact of health latent classes and socio-demographic factors on outpatient and inpatient utilization. Results: Among 2,981 participants aged 65 and over without missing health indictors, four health latent classes were identified and labeled as “Lacking Socialization” (10.4%), “High Comorbidity” (16.7%), “Frail Group” (7.7%), and “Relatively Healthy” (65.1%). Among 1,974 participants with complete information, compared with the Relatively Healthy group, those in the Lacking Socialization group costed more inpatient expenditure (p-value =0.02). Those in the High Comorbidity and Frail groups tended to use healthcare services and costed more outpatient expenditure (all p-value <0.01). After controlling for health latent classes, the effects of age, gender, marital status, education, residence area, occupation, and health insurance on healthcare utilization were significant. Conclusions: Four heterogeneous health latent classes were identified by multidimensional health, and had significant effects on healthcare utilization. After controlling for health latent classes, different effects of socio-demographic factors on healthcare utilization were found. It enhances our understanding of heterogeneous health and complex healthcare demands in older Chinese, and is valuable for improving healthcare resource allocation targeted for healthy aging.

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Huang, Zimei, Tinghui Li et Mark Xu. « Are There Heterogeneous Impacts of National Income on Mental Health ? » International Journal of Environmental Research and Public Health 17, n^o 20 (16 octobre 2020) : 7530. http://dx.doi.org/10.3390/ijerph17207530.

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Understanding heterogeneous impact and mechanisms between national income and mental health are crucial to develop prevention and intervention strategies. Based on panel data from 2007 to 2017, this study explores the heterogeneous impact of national income on different types of mental health. Then, it analyzes the heterogeneous impact among countries with different income levels. Furthermore, the heterogeneous moderating effects of national income on mental health mechanisms are elaborated and the findings reveal several key conclusions: firstly, national income exerts a heterogeneous impact on different types of mental health. Rising national income is conducive to increase people’s happiness and reduce their prevalence of anxiety disorders, but it increases the prevalence of depression disorders. Secondly, national income has a heterogeneous impact on different types of mental health among countries with different income levels. Furthermore, the heterogeneous influence mechanism of national income on mental health is mainly reflected in different types of mental health. Unemployment, social support and freedom can moderate the relationship between national income and depression, while social support, positive affect and negative affect can moderate the relationship between national income and anxiety. Finally, based on the conclusions of quantitative analysis, some important policy recommendations are proposed for policy makers.

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Glinskiy, Vladimir, Elizaveta Freidina, Lyudmila Serga et Kirill Zaykov. « Rational data selection from heterogeneous information space : problem statement ». E3S Web of Conferences 458 (2023) : 09007. http://dx.doi.org/10.1051/e3sconf/202345809007.

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The paper considers the problem of providing rational data selection from heterogeneous information space for adaptive-heuristic intelligence embedded in the mechanism of robust management of socio-economic systems. The relevance of solving the presented problem is determined by the need to adapt the work of the socio-economic system to the paradigm of modernity -sustainable functioning and development in conditions of accelerated changes. Systems move from complex to complex-adaptive, diffuse forms with the development of the ability to self-regulate like living, biological systems. To survive and develop, they try to make flexible, anticipatory adaptations within the limits of acceptable deviations of physiological parameters from some parameters -constants of health, which represent the conditions of maintaining homeostasis. The prototype is the automatic robust control of closed systems, in which the leading parameters are given a certain degree of freedom and the limit of their permissible changes is set. The effectiveness of such control strongly depends on the reliability of the data -facts entered the information granules of adaptive and robust homeostasis. We propose a technology for obtaining reliable information about the state of the studied objects for robust management based on the use of rational choice methods.

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Mazouz, Sanae, OuÃ§amah Mohammed Cherkaoui Malki et El Habib Nfaoui. « Design and implementation of a health document ». INTERNATIONAL JOURNAL OF COMPUTERS & ; TECHNOLOGY 14, n^o 11 (6 août 2015) : 6219–28. http://dx.doi.org/10.24297/ijct.v14i11.1809.

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Exchanging and integrating medical information in the healthcare domain is a challenge. Indeed, the diversity of databases and the different representations of information sources make this exchange a very difficult task. Divers standards, (e.g. HL7: Health Level Seven; DICOM: Digital Imaging and Communication in Medicine), are created to enable the exchange and make health information systems interoperable. However, applying standardization requires changing the structure of existing healthcare systems. Our main purpose is to create a health document for exchanging health information between heterogeneous systems without applying changes on the internal structure of systems. The document uses the XML language to allow a structured and flexible exchange of healthcare data. The proposed health document can make the exchange of healthcare data among heterogeneous health information systems simpler and efficient. This document addresses the problem of interoperability between health information systems. The paper summarizes standards used to support interoperability in healthcare domain and propose a health document to enable the exchange of medical information across heterogeneous and distributed health information systems without requirements or adjustment on their systems.

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Jesus, M. A., et Vania Estrela. « An Introduction to Data Mining Applied to Health-Oriented Databases ». Oriental journal of computer science and technology 9, n^o 3 (20 décembre 2016) : 177–85. http://dx.doi.org/10.13005/ojcst/09.03.03.

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The application of data mining (DM) in healthcare is increasing. Healthcare organizations generate and collect large voluminous and heterogeneous information daily and DM helps to uncover some interesting patterns, which leads to the manual tasks elimination, easy data extraction directly from records, to save lives, to reduce the cost of medical services and to enable early detection of diseases. These patterns can help healthcare specialists to make forecasts, put diagnoses, and set treatments for patients in health facilities. This work overviews DM methods and main issues. Three case studies illustrate DM in healthcare applications: (i) In-Vitro Fertilization; (ii) Content-Based Image Retrieval (CBIR); and (iii) Organ transplantation.

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Dobrzykowski, David D. « Examining Heterogeneous Patterns of Electronic Health Records Use ». International Journal of Healthcare Information Systems and Informatics 7, n^o 2 (avril 2012) : 1–16. http://dx.doi.org/10.4018/jhisi.2012040101.

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The basic use of Electronic Health Records (EHR) and the progression toward advanced EHR applications are key concerns facing leaders interested in integrating the healthcare delivery supply chain. Currently, substantial heterogeneity exists among hospitals in terms of EHR use and the progression toward advanced EHR applications. Understanding this heterogeneity is important as hospitals face pressure to adopt and achieve meaningful use of the technology. Contingency theory is tested herein to suggest that a hospital’s structural constraints may explain the heterogeneity among hospitals in terms of their EHR use. Data collected from 297 acute care hospitals in 47 states reveals that critical access hospitals may be slow to use EHR, even in basic applications. Conversely, major teaching hospitals appear to be early adopters, achieving advanced EHR use. These findings are important for hospital executives, Health Information Technology managers, and policymakers concerned with directing resources with an aim toward EHR integration.

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Kim, Sein, Namkyeong Lee, Junseok Lee, Dongmin Hyun et Chanyoung Park. « Heterogeneous Graph Learning for Multi-Modal Medical Data Analysis ». Proceedings of the AAAI Conference on Artificial Intelligence 37, n^o 4 (26 juin 2023) : 5141–50. http://dx.doi.org/10.1609/aaai.v37i4.25643.

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Routine clinical visits of a patient produce not only image data, but also non-image data containing clinical information regarding the patient, i.e., medical data is multi-modal in nature. Such heterogeneous modalities offer different and complementary perspectives on the same patient, resulting in more accurate clinical decisions when they are properly combined. However, despite its significance, how to effectively fuse the multi-modal medical data into a unified framework has received relatively little attention. In this paper, we propose an effective graph-based framework called HetMed (Heterogeneous Graph Learning for Multi-modal Medical Data Analysis) for fusing the multi-modal medical data. Specifically, we construct a multiplex network that incorporates multiple types of non-image features of patients to capture the complex relationship between patients in a systematic way, which leads to more accurate clinical decisions. Extensive experiments on various real-world datasets demonstrate the superiority and practicality of HetMed. The source code for HetMed is available at https://github.com/Sein-Kim/Multimodal-Medical.

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Peng, Cong, et Prashant Goswami. « Meaningful Integration of Data from Heterogeneous Health Services and Home Environment Based on Ontology ». Sensors 19, n^o 8 (12 avril 2019) : 1747. http://dx.doi.org/10.3390/s19081747.

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The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future. This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable.

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Garg, Neha, et Sunidhi Shrivastava. « A Multi-View Learning based Clustering Method for Health Care System ». International Journal for Research in Applied Science and Engineering Technology 10, n^o 5 (31 mai 2022) : 3853–59. http://dx.doi.org/10.22214/ijraset.2022.43243.

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Abstract: Patients in hospitals have faced complications due to measurement errors, missing data, privacy issues etc. in electronic medical records. However, these medical records from heterogeneous sources have both structured and unstructured data. In particular, unstructured clinical data is valuable source of information including patient’s records of pathology data, radiology findings, medication order etc. However, to scrutinize, construe and presentation of this unstructured and high dimensional data is one of the significant modeling challenge that clinical support system has faced from many years before. Therefore, there is a need of some standard technique to locate both subjective and objective guesstimates of patient’s condition. Our endowments in this paper are twofold. First, present a multi-view learning technique, i.e. Collective Matrix Factorization to combine the extracted features from multiple views and gives a low dimensional representation of combined clinical data. Second, proposed a Genetic-K-means based clustering algorithm based on Collective Matrix Factorization for heterogeneous clinical records. It has been observed by the experiments that proposed method gives more accurate clustering results than existing method. Keywords: Clinical notes; Collective Matrix Factorization; Genetic; heterogeneous data; K-means; Multi-view learning.

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Garg, Neha, et Sunidhi Shrivastava. « A Multi-View Learning based Clustering Method for Health Care System ». International Journal for Research in Applied Science and Engineering Technology 10, n^o 5 (31 mai 2022) : 3853–59. http://dx.doi.org/10.22214/ijraset.2022.43243.

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Abstract: Patients in hospitals have faced complications due to measurement errors, missing data, privacy issues etc. in electronic medical records. However, these medical records from heterogeneous sources have both structured and unstructured data. In particular, unstructured clinical data is valuable source of information including patient’s records of pathology data, radiology findings, medication order etc. However, to scrutinize, construe and presentation of this unstructured and high dimensional data is one of the significant modeling challenge that clinical support system has faced from many years before. Therefore, there is a need of some standard technique to locate both subjective and objective guesstimates of patient’s condition. Our endowments in this paper are twofold. First, present a multi-view learning technique, i.e. Collective Matrix Factorization to combine the extracted features from multiple views and gives a low dimensional representation of combined clinical data. Second, proposed a Genetic-K-means based clustering algorithm based on Collective Matrix Factorization for heterogeneous clinical records. It has been observed by the experiments that proposed method gives more accurate clustering results than existing method. Keywords: Clinical notes; Collective Matrix Factorization; Genetic; heterogeneous data; K-means; Multi-view learning.

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Salama, Mohamed, Hatem Abdelkader et Amira Abdelwahab. « A novel ensemble approach for heterogeneous data with active learning ». International Journal of Engineering Business Management 14 (janvier 2022) : 184797902210826. http://dx.doi.org/10.1177/18479790221082605.

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At present, millions of internet users are contributing a huge amount of data. This data is extremely heterogeneous, and so, it is hard to analyze and derive information from this data that is considered an indispensable source for decision-makers. Due to this massive growth, the classification of data and analysis has become an important research subject. Extracting information from this data has become a necessity. As a result, it was necessary to process these enormous volumes of data to uncover hidden information and therefore improve data analysis and, in turn, classification accuracy. In this paper, firstly, we focus on developing an ensemble machine-learning model based on active learning which identifies the most effective feature extraction strategy for heterogeneous data analysis, and compare it with traditional machine-learning algorithms. Secondly, we evaluate the proposed model during the experiments; five heterogeneous datasets from various domains were used, such as a Health Care Reform dataset, Sander Frandsen dataset, Financial Phrase Bank dataset, SMS Spam Collection dataset, and Textbook sales dataset. According to the results, the novel approach for data analysis performed better than conventional methods. Finally, the study’s findings confirmed the validity of the suggested technique, meeting the study’s goal of using ensemble methods with active learning to raise the model’s overall accuracy for effectively classifying and analyzing heterogeneous data, reducing the time and money spent training the model, and delivering superior analysis performance as well as insights into other elements of extracting information from heterogeneous data.

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Smith, Valerie A., et John S. Preisser. « A marginalized two-part model with heterogeneous variance for semicontinuous data ». Statistical Methods in Medical Research 28, n^o 5 (16 février 2018) : 1412–26. http://dx.doi.org/10.1177/0962280218758358.

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Semicontinuous data, characterized by a point mass at zero followed by a positive, continuous distribution, arise frequently in medical research. These data are typically analyzed using two-part mixtures that separately model the probability of incurring a positive outcome and the distribution of positive values among those who incur them. In such a conditional specification, however, standard two-part models do not provide a marginal interpretation of covariate effects on the overall population. We have previously proposed a marginalized two-part model that yields more interpretable effect estimates by parameterizing the model in terms of the marginal mean. In the original formulation, a constant variance was assumed for the positive values. We now extend this model to a more general framework by allowing non-constant variance to be explicitly modeled as a function of covariates, and incorporate this variance into two flexible distributional assumptions, log-skew-normal and generalized gamma, both of which take the log-normal distribution as a special case. Using simulation studies, we compare the performance of each of these models with respect to bias, coverage, and efficiency. We illustrate the proposed modeling framework by evaluating the effect of a behavioral weight loss intervention on health care expenditures in the Veterans Affairs health system.

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Kim, Youngjune, Bowen Chen, Allen M. Featherstone et Dustin L. Pendell. « Are Efficient and Inefficient Farms Heterogeneous ? Evidence from Kansas Farms ». Korean Agricultural Economics Association 64, n^o 1 (30 mars 2023) : 103–15. http://dx.doi.org/10.24997/kjae.2023.64.1.103.

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Most of the previous literature on efficiency assumes that efficient and inefficient farms are homogeneous in production, even though they may have different strategies to increase efficiency. Exploiting Super Data envelopment analysis (DEA) and quantile regression, this study examines the sources of efficiency with particular consideration of the heterogeneity between efficient and inefficient farms using a farm-level dataset. The results show that an increase in some farm characteristics, such as the number of beef cows, percentage of income from beef cows, and percentage of acres owned, affects efficiency in different ways for efficient and inefficient farms. These results imply that efficient and inefficient farms are heterogeneous in terms of how farm characteristics affect efficiency.

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Jeong, Hoyeon, Yong W. Jeong, Yeonjae Park, Kise Kim, Junghwan Park et Dae R. Kang. « Applications of deep learning methods in digital biomarker research using noninvasive sensing data ». DIGITAL HEALTH 8 (janvier 2022) : 205520762211366. http://dx.doi.org/10.1177/20552076221136642.

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Introduction: Noninvasive digital biomarkers are critical elements in digital healthcare in terms of not only the ease of measurement but also their use of raw data. In recent years, deep learning methods have been put to use to analyze these diverse heterogeneous data; these methods include representation learning for feature extraction and supervised learning for the prediction of these biomarkers. Methods: We introduce clinical cases of digital biomarkers and various deep-learning methods applied according to each data type. In addition, deep learning methods for the integrated analysis of multidimensional heterogeneous data are introduced, and the utility of these data as an integrated digital biomarker is presented. The current status of digital biomarker research is examined by surveying research cases applied to various types of data as well as modeling methods. Results: We present a future research direction for using data from heterogeneous sources together by introducing deep learning methods for dimensionality reduction and mode integration from multimodal digital biomarker studies covering related domains. The integration of multimodality has led to advances in research through the improvement of performance and complementarity between modes. Discussion: The integrative digital biomarker will be more useful for research on diseases that require data from multiple sources to be treated together. Since delicate signals from patients are not missed and the interaction effects between signals are also considered, it will be helpful for immediate detection and more accurate prediction of symptoms.

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Mukhiya, Suresh Kumar, et Yngve Lamo. « An HL7 FHIR and GraphQL approach for interoperability between heterogeneous Electronic Health Record systems ». Health Informatics Journal 27, n^o 3 (juillet 2021) : 146045822110439. http://dx.doi.org/10.1177/14604582211043920.

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Heterogeneities in data representation and care processes create interoperability complexity among Electronic Health Record systems (EHRs). We can resolve such data and process level heterogeneities by following consistent healthcare standards like Clinical Document Architecture (CDA), OpenEHR, and HL7 FHIR. However, these standards also differ at the structural and implementation level, making interoperability more complex. Hence, there is a need to investigate mechanisms that can resolve data level heterogeneity to achieve semantic data interoperability between heterogeneous systems. As a solution to this, we offer an architecture that utilizes a resource server based on GraphQL and HL7 FHIR that establishes communication between two heterogeneous EHRs. This paper describes how the proposed architecture is implemented to achieve interoperability between two heterogeneous EHRs, HL7 FHIR and OpenMRS. The presented approach establishes secure communication between the EHRs and provides accurate mappings that enable timely health information exchange between EHRs.

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Zhang, Zhi, et Min Hong. « Research on the heterogeneous effects of residents' income on mental health ». Mathematical Biosciences and Engineering 20, n^o 3 (2023) : 5043–65. http://dx.doi.org/10.3934/mbe.2023234.

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<abstract> <p>The influence of residents' income on mental health is complex, and there are heterogeneous effects of residents' income on different types of mental health. Based on the annual panel data of 55 countries from 2007 to 2019, this paper divides residents' income into three dimensions: absolute income, relative income and income gap. Mental health is divided into three aspects: subjective well-being, prevalence of depression and prevalence of anxiety. Panel Tobit model is used to study the heterogeneous impact of residents' income on mental health. The results show that, on the one hand, different dimensions of residents' income have a heterogeneous impact on mental health, specifically, absolute income has a positive impact on mental health, while relative income and income gap have no significant impact on mental health. On the other hand, the impact of different dimensions of residents' income on different types of mental health is heterogeneous. Specifically, absolute income and income gap have heterogeneous effects on different types of mental health, while relative income has no significant impact on different types of mental health.</p> </abstract>

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Sükei, Emese, Agnes Norbury, M. Mercedes Perez-Rodriguez, Pablo M. Olmos et Antonio Artés. « Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data : Data-Driven Machine Learning Approach ». JMIR mHealth and uHealth 9, n^o 3 (22 mars 2021) : e24465. http://dx.doi.org/10.2196/24465.

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Background Mental health disorders affect multiple aspects of patients’ lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient’s mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues. Objective This study aims to present a machine learning–based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations. Methods Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days’ worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient. Results Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals’ overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days’ data. Conclusions These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients’ mood states.

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Ronquillo, Jay G., J. Erik Winterholler, Kamil Cwikla, Raphael Szymanski et Christopher Levy. « Health IT, hacking, and cybersecurity : national trends in data breaches of protected health information ». JAMIA Open 1, n^o 1 (11 juin 2018) : 15–19. http://dx.doi.org/10.1093/jamiaopen/ooy019.

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Abstract Objective The rapid adoption of health information technology (IT) coupled with growing reports of ransomware, and hacking has made cybersecurity a priority in health care. This study leverages federal data in order to better understand current cybersecurity threats in the context of health IT. Materials and Methods Retrospective observational study of all available reported data breaches in the United States from 2013 to 2017, downloaded from a publicly available federal regulatory database. Results There were 1512 data breaches affecting 154 415 257 patient records from a heterogeneous distribution of covered entities (P < .001). There were 128 electronic medical record-related breaches of 4 867 920 patient records, while 363 hacking incidents affected 130 702 378 records. Discussion and Conclusion Despite making up less than 25% of all breaches, hacking was responsible for nearly 85% of all affected patient records. As medicine becomes increasingly interconnected and informatics-driven, significant improvements to cybersecurity must be made so our health IT infrastructure is simultaneously effective, safe, and secure.

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Horvath, M. M., S. A. Rusincovitch et R. L. Richesson. « Clinical Research Informatics and Electronic Health Record Data ». Yearbook of Medical Informatics 23, n^o 01 (août 2014) : 215–23. http://dx.doi.org/10.15265/iy-2014-0009.

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Summary Objectives: The goal of this survey is to discuss the impact of the growing availability of electronic health record (EHR) data on the evolving field of Clinical Research Informatics (CRI), which is the union of biomedical research and informatics. Results: Major challenges for the use of EHR-derived data for research include the lack of standard methods for ensuring that data quality, completeness, and provenance are sufficient to assess the appropriateness of its use for research. Areas that need continued emphasis include methods for integrating data from heterogeneous sources, guidelines (including explicit phenotype definitions) for using these data in both pragmatic clinical trials and observational investigations, strong data governance to better understand and control quality of enterprise data, and promotion of national standards for representing and using clinical data. Conclusions: The use of EHR data has become a priority in CRI. Awareness of underlying clinical data collection processes will be essential in order to leverage these data for clinical research and patient care, and will require multi-disciplinary teams representing clinical research, informatics, and healthcare operations. Considerations for the use of EHR data provide a starting point for practical applications and a CRI research agenda, which will be facilitated by CRI’s key role in the infrastructure of a learning healthcare system.

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Venkateswara Reddy, R., et Dr D. Murali. « Analyzing Indian healthcare data with big data ». International Journal of Engineering & ; Technology 7, n^o 3.29 (24 août 2018) : 88. http://dx.doi.org/10.14419/ijet.v7i3.29.18467.

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Big Data is the enormous amounts of data, being generated at present times. Organizations are using this Big Data to analyze and predict the future to make profits and gain competitive edge in the market. Big Data analytics has been adopted into almost every field, retail, banking, governance and healthcare. Big Data can be used for analyzing healthcare data for better planning and better decision making which lead to improved healthcare standards. In this paper, Indian health data from 1950 to 2015 are analyzed using various queries. This healthcare generates the considerable amount of heterogeneous data. But without the right methods for data analysis, these data have become useless. The Big Data analysis with Hadoop plays an active role in performing significant real-time analyzes of the enormous amount of data and able to predict emergency situations before this happens.

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Liu, Weihao, Linhai Jiang, Xitong Luo et Shiwei Guo. « Experimental Study on Heterogeneous data storage and exchange abstract storage middleware ». Journal of Computing and Electronic Information Management 12, n^o 2 (30 mars 2024) : 89–91. http://dx.doi.org/10.54097/x19dp7bd.

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Some Internet companies on the market do not have rich experience in database development, and there are certain problems in the face of sudden, urgent and high concurrent requirements like epidemic health code. Hope that through the interface provided by this middleware, the data can be automatically split according to certain rules. Using the advantages of different architecture databases, the data were stored in different architecture databases to achieve data heterogeneity. And it can extract, combine and return data from different databases, abstract the process of database access and access. The decoupling of database and business code is realized to solve the pain point of high-performance product development difficulty.

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Soguero-Ruiz, Cristina, Kristian Hindberg, Inmaculada Mora-Jiménez, José Luis Rojo-Álvarez, Stein Olav Skrøvseth, Fred Godtliebsen, Kim Mortensen et al. « Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods ». Journal of Biomedical Informatics 61 (juin 2016) : 87–96. http://dx.doi.org/10.1016/j.jbi.2016.03.008.

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