{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:48:55Z","timestamp":1785545335036,"version":"3.56.0"},"reference-count":136,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T00:00:00Z","timestamp":1747353600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T00:00:00Z","timestamp":1747353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"NTNU Norwegian University of Science and Technology"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>This study aims to understand how secondary use of health records can be done for prediction, detection, treatment recommendations, and related tasks in clinical decision support systems.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>Articles mentioning the secondary use of EHRs for clinical utility, specifically in prediction, detection, treatment recommendations, and related tasks in decision support were reviewed. We extracted study details, methods, tools, technologies, utility, and performance.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We found that secondary uses of EHRs are primarily retrospective, mostly conducted using records from hospital EHRs, EHR data networks, and warehouses. EHRs vary in type and quality, making it critical to ensure their completeness and quality for clinical utility. Widely used methods include machine learning, statistics, simulation, and analytics. Secondary use of health records can be applied in any area of medicine. The selection of data, cohorts, tools, technology, and methods depends on the specific clinical utility.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>The process for secondary use of health records should include three key steps: 1. Validation of the quality of EHRs, 2. Use of methods, tools, and technologies with proactive training, and 3. Multidimensional assessment of the results and their usefulness.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Trial Registration<\/jats:title>\n            <jats:p>: PROSPERO registration number CRD42023409582<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12911-025-03021-8","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T08:54:42Z","timestamp":1747385682000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Secondary use of health records for prediction, detection, and treatment planning in the clinical decision support system: a systematic review"],"prefix":"10.1186","volume":"25","author":[{"given":"Dipendra","family":"Pant","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00d8ystein","family":"Nytr\u00f8","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bennett L.","family":"Leventhal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carolyn","family":"Clausen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaban","family":"Koochakpour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Line","family":"Stien","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Odd Sverre","family":"Westbye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roman","family":"Koposov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas Brox","family":"R\u00f8st","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Frodl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Norbert","family":"Skokauskas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,16]]},"reference":[{"key":"3021_CR1","unstructured":"Ehrenstein V, et al. Obtaining Data from Electronic Health Records, in Tools and Technologies for Registry Interoperability, Registries for Evaluating Patient Outcomes: a User\u2019s Guide, 3rd Addendum 2 [Internet]. 2019. Agency for Healthcare Research and Quality (US)."},{"key":"3021_CR2","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1197\/jamia.M2025","volume":"13","author":"PC Tang","year":"2006","unstructured":"Tang PC, et al. Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption. J Am Med Inf Assoc. 2006;13:121\u201326.","journal-title":"J Am Med Inf Assoc"},{"issue":"3","key":"3021_CR3","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1136\/jamia.2010.003673","volume":"17","author":"JR Vest","year":"2010","unstructured":"Vest JR, Gamm LD.Health information exchange: persistent challenges and new strategies. J Am Med Inform Assoc: JAMIA. 2010;17(3):288.","journal-title":"J Am Med Inform Assoc: JAMIA"},{"issue":"5","key":"3021_CR4","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.ijmedinf.2007.09.001","volume":"77","author":"K H\u00e4yrinen","year":"2008","unstructured":"H\u00e4yrinen K, Saranto K, Nyk\u00e4nen P.Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform. 2008;77(5):291\u2013304.","journal-title":"Int J Med Inform"},{"key":"3021_CR5","doi-asserted-by":"crossref","unstructured":"Colombo F, Oderkirk J, Slawomirski L. Health information systems, electronic medical records, and big data in global healthcare: progress and challenges in oecd countries. Handbook Glob Health. 2020;1\u201331.","DOI":"10.1007\/978-3-030-05325-3_71-1"},{"key":"3021_CR6","unstructured":"WHO. Meeting on secondary use of health data 2022 (23 July 2023); Available from: https:\/\/www.who.int\/europe\/news-room\/events\/item\/2022\/12\/13\/default-calendar\/meeting-on-secondary-use-of-health-data."},{"key":"3021_CR7","first-page":"136947","volume-title":"Secondary Use of Electronic Health Record: opportunities and Challenges","author":"SM Shah","year":"2020","unstructured":"Shah SM, Khan RA. Secondary Use of Electronic Health Record: opportunities and Challenges. vol. 8. IEEE access; 2020. p. 136947\u201365."},{"key":"3021_CR8","first-page":"1","volume-title":"Economic Impact of Clinical Decision Support Interventions Based on Electronic Health Records","author":"D Lewkowicz","year":"2020","unstructured":"Lewkowicz D, Wohlbrandt A, Boettinger E. Economic Impact of Clinical Decision Support Interventions Based on Electronic Health Records. vol. 20. BMC Health Services Research; 2020. p. 1\u201312."},{"issue":"10","key":"3021_CR9","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1001\/archinternmed.2010.527","volume":"171","author":"MJ Romano","year":"2011","unstructured":"Romano MJ, Stafford RS.Electronic health records and clinical decision support systems: impact on national ambulatory care quality. Arch Intern Med. 2011;171(10):897\u2013903.","journal-title":"Arch Intern Med"},{"key":"3021_CR10","doi-asserted-by":"publisher","first-page":"e2238231","DOI":"10.1001\/jamanetworkopen.2022.38231","volume":"5","author":"SJ Weiner","year":"2022","unstructured":"Weiner SJ, et al. Effect of electronic health record clinical decision support on contextualization of care: a randomized clinical trial. JAMA Netw Open 2022;5:e2238231\u2013e2238231.","journal-title":"JAMA Netw Open"},{"key":"3021_CR11","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1192\/bjp.bp.113.142612","volume":"206","author":"C Hollis","year":"2015","unstructured":"Hollis C, et al. Technological innovations in mental healthcare: harnessing the digital revolution. Br J Psychiatry 2015;206:263\u201365.","journal-title":"Br J Psychiatry"},{"key":"3021_CR12","doi-asserted-by":"crossref","unstructured":"Berner ES, La Lande TJ. Overview of clinical decision support systems. Clinical decision support systems: theory and practice. 2016:1\u201317.","DOI":"10.1007\/978-3-319-31913-1_1"},{"key":"3021_CR13","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1007\/978-3-030-58721-5_24","volume-title":"Clinical Decision-support Systems, in Biomedical Informatics: computer Applications in Health Care and Biomedicine","author":"MA Musen","year":"2021","unstructured":"Musen MA, Middleton B, Greenes RA. Clinical Decision-support Systems, in Biomedical Informatics: computer Applications in Health Care and Biomedicine. Springer, 2021:795\u2013840."},{"key":"3021_CR14","unstructured":"Agency\u2019s EM GDPR and the secondary use of health data Report from EMA workshop held with the EMA Patients\u2019 and Consumers\u2019 Working Party (PCWP) and Healthcare Professionals\u2019 Working Party (HCPWP) 2020 (20 August 2023); Available from: https:\/\/www.ema.europa.eu\/en\/documents\/report\/report-workshop-application-general-data-protection-regulation-gdpr-area-health-and-secondary-use-data-medicines-and-public-health-purposes_en.pdf."},{"key":"3021_CR15","unstructured":"Cheng HG, Phillips MR. Secondary analysis of existing data: opportunities and implementation. Shanghai archives of psychiatry. 2014;26(6):371."},{"key":"3021_CR16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-019-1002-x","volume":"20","author":"CE Clausen","year":"2020","unstructured":"Clausen CE, et al. Testing an individualized digital decision assist system for the diagnosis and management of mental and behavior disorders in children and adolescents. BMC Med Inf Decis Making. 2020;20:1\u20139.","journal-title":"BMC Med Inf Decis Making"},{"key":"3021_CR17","doi-asserted-by":"crossref","unstructured":"Page MJ, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Bmj. 2021;372.","DOI":"10.1136\/bmj.n71"},{"key":"3021_CR18","doi-asserted-by":"publisher","first-page":"793316","DOI":"10.3389\/fdgth.2022.793316","volume":"4","author":"S Bozkurt","year":"2022","unstructured":"Bozkurt S, et al. Expanding the secondary use of prostate cancer real world data: automated classifiers for clinical and pathological stage. Front Digit Health. 2022;4:793316.","journal-title":"Front Digit Health"},{"key":"3021_CR19","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1093\/jamia\/ocx098","volume":"25","author":"MR Hribar","year":"2018","unstructured":"Hribar MR, et al. Secondary use of electronic health record data for clinical workflow analysis. J Am Med Inf Assoc 2018;25:40\u201346.","journal-title":"J Am Med Inf Assoc"},{"key":"3021_CR20","unstructured":"Lin W-C, et al. Secondary use of electronic health record data for prediction of outpatient visit length in ophthalmology clinics. in AMIA Annual Symposium Proceedings. 2018. American Medical Informatics Association."},{"key":"3021_CR21","doi-asserted-by":"publisher","first-page":"e35475","DOI":"10.2196\/35475","volume":"10","author":"D Hu","year":"2022","unstructured":"Hu D, et al. Using natural language processing and machine learning to preoperatively predict lymph node metastasis for non\u2013small cell lung cancer with electronic medical records: development and validation study. JMIR Med Inform 2022;10:e35475.","journal-title":"JMIR Med Inform"},{"key":"3021_CR22","unstructured":"Tang Y, et al. Prediction of type II diabetes onset with computed tomography and electronic medical records. In Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures: 10th International Workshop, ML-CDS 2020, and 9th International Workshop, CLIP 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4\u20138, 2020, Proceedings 9. 2020. Springer."},{"key":"3021_CR23","doi-asserted-by":"publisher","first-page":"e0226718","DOI":"10.1371\/journal.pone.0226718","volume":"15","author":"Q Wang","year":"2020","unstructured":"Wang Q, et al. Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network. PLoS One 2020;15:e0226718.","journal-title":"PLoS One"},{"key":"3021_CR24","doi-asserted-by":"publisher","first-page":"20190255","DOI":"10.1259\/bjr.20190255","volume":"92","author":"L Lin","year":"2019","unstructured":"Lin L, et al. Development and implementation of a dynamically updated big data intelligence platform from electronic health records for nasopharyngeal carcinoma research. Br J Radiol 2019;92:20190255.","journal-title":"Br J Radiol"},{"key":"3021_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep26094","volume":"6","author":"R Miotto","year":"2016","unstructured":"Miotto R, et al. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep 2016;6:1\u201310.","journal-title":"Sci Rep"},{"key":"3021_CR26","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.jbi.2017.04.008","volume":"69","author":"SN Kasthurirathne","year":"2017","unstructured":"Kasthurirathne SN, et al. Toward better public health reporting using existing off the shelf approaches: the value of medical dictionaries in automated cancer detection using plaintext medical data. J Biomed Informat. 2017;69:160\u201376.","journal-title":"J Biomed Informat"},{"issue":"2","key":"3021_CR27","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1111\/jjns.12103","volume":"13","author":"S Yokota","year":"2016","unstructured":"Yokota S, Ohe K.Construction and evaluation of FiND, a fall risk prediction model of inpatients from nursing data. Japan J Nurs Sci. 2016;13(2):247\u201355.","journal-title":"Japan J Nurs Sci"},{"key":"3021_CR28","first-page":"190","volume-title":"Physician and Departmental Performance Metrics in Pediatric Emergency Care: secondary Use of Patient Visit Data","author":"B Taylor","year":"2015","unstructured":"Taylor B, MacPhee S. Physician and Departmental Performance Metrics in Pediatric Emergency Care: secondary Use of Patient Visit Data. vol. 63. Procedia Computer Science; 2015. p. 190\u201397."},{"key":"3021_CR29","doi-asserted-by":"publisher","first-page":"e288","DOI":"10.1136\/amiajnl-2013-001923","volume":"20","author":"JT Fern\u00e1ndez-Breis","year":"2013","unstructured":"Fern\u00e1ndez-Breis JT, et al. Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts. J Am Med Inf Assoc 2013;20:e288\u2013e296.","journal-title":"J Am Med Inf Assoc"},{"key":"3021_CR30","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1093\/jamia\/ocx160","volume":"25","author":"H Wu","year":"2018","unstructured":"Wu H, et al. SemEHR: a general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research. J Am Med Inf Assoc 2018;25:530\u201337.","journal-title":"J Am Med Inf Assoc"},{"key":"3021_CR31","doi-asserted-by":"crossref","unstructured":"Ouzzani M, et al. Rayyan\u2014a web and mobile app for systematic reviews. Systematic reviews. 2016;5:1\u201310.","DOI":"10.1186\/s13643-016-0384-4"},{"key":"3021_CR32","unstructured":"Ought. Elicit: The AI Research Assistant. 2023 (22 February 2023); Available from: https:\/\/elicit.org."},{"key":"3021_CR33","volume-title":"Natural Language Processing with Python: analyzing Text with the Natural Language Toolkit","author":"S Bird","year":"2009","unstructured":"Bird S, Klein E, Loper E. Natural Language Processing with Python: analyzing Text with the Natural Language Toolkit. \u201cO\u2019Reilly Media, Inc.\u201d; 2009."},{"key":"3021_CR34","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995;20:273\u201397.","journal-title":"Machine Learning"},{"key":"3021_CR35","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1016\/j.jbi.2009.05.002","volume":"42","author":"H Harkema","year":"2009","unstructured":"Harkema H, et al. ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports. J Biomed Informat 2009;42:839\u201351.","journal-title":"J Biomed Informat"},{"key":"3021_CR36","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1006\/jbin.2001.1029","volume":"34","author":"WW Chapman","year":"2001","unstructured":"Chapman WW, et al. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Informat 2001;34:301\u201310.","journal-title":"J Biomed Informat"},{"key":"3021_CR37","unstructured":"Team RC, R: a language and environment for statistical computing."},{"key":"3021_CR38","unstructured":"Inc., A.. Apple. Numbers. 2021."},{"key":"3021_CR39","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-7138-7","volume-title":"An Introduction to Statistical Learning","author":"G James","year":"2013","unstructured":"James G, et al. An Introduction to Statistical Learning, vol. 112. Springer. 2013."},{"key":"3021_CR40","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Machine Learning. 2001;45:5\u201332.","journal-title":"Machine Learning"},{"key":"3021_CR41","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"3021_CR42","unstructured":"Ke G, et al. Lightgbm: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst. 2017;30."},{"key":"3021_CR43","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","volume":"17","author":"P Virtanen","year":"2020","unstructured":"Virtanen P, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 2020;17:261\u201372.","journal-title":"Nat Methods"},{"key":"3021_CR44","unstructured":"Devlin J, et al., Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018."},{"key":"3021_CR45","doi-asserted-by":"publisher","DOI":"10.1002\/9781118548387","volume-title":"Applied Logistic Regression","author":"DW Hosmer Jr","year":"2013","unstructured":"Hosmer Jr DW, Lemeshow S, Sturdivant RX. Applied Logistic Regression. John Wiley & Sons; 2013."},{"key":"3021_CR46","doi-asserted-by":"crossref","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015;521(7553):436\u201344.","DOI":"10.1038\/nature14539"},{"key":"3021_CR47","unstructured":"Paszke A, et al. Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst. 2019;32."},{"key":"3021_CR48","unstructured":"Nvidia, CUDA. 2006."},{"key":"3021_CR49","unstructured":"Chaganti S, et al. Contextual deep regression network for volume estimation in orbital CT. In Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part VI 22. 2019. Springer."},{"key":"3021_CR50","unstructured":"Kingma DP, Ba J, Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014."},{"key":"3021_CR51","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19. 2016. Springer.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"3021_CR52","doi-asserted-by":"crossref","unstructured":"Bezdek JC, Ehrlich R, Full W. FCM: the fuzzy c-means clustering algorithm. Computers & geosciences. 1984;10(2-3):191\u2013203.","DOI":"10.1016\/0098-3004(84)90020-7"},{"key":"3021_CR53","first-page":"574","volume":"216","author":"G Hripcsak","year":"2015","unstructured":"Hripcsak G, et al. Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers. Stud Health Technol Inform. 2015;216:574.","journal-title":"Stud Health Technol Inform"},{"key":"3021_CR54","unstructured":"OHDSI. Observational health data sciences and informatics. OMOP Common Data Model. 2014."},{"issue":"1","key":"3021_CR55","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R.Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Stat Method. 1996;58(1):267\u201388.","journal-title":"J R Stat Soc Ser B Stat Method"},{"key":"3021_CR56","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1136\/amiajnl-2011-000523","volume":"19","author":"MA Musen","year":"2012","unstructured":"Musen MA, et al. The national center for biomedical ontology. J Am Med Inf Assoc 2012;19:190\u201395.","journal-title":"J Am Med Inf Assoc"},{"key":"3021_CR57","unstructured":"Jonquet C, Shah NH, Musen MA. The open biomedical annotator. Summit on Trans Bioinform. 2009;2009:56."},{"key":"3021_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447755","volume":"54","author":"FL Gewers","year":"2021","unstructured":"Gewers FL, et al. Principal component analysis: a natural approach to data exploration. ACM Computing Surveys (CSUR) 2021;54:1\u201334.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"3021_CR59","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/978-0-387-73003-5_196","volume":"741","author":"DA Reynolds","year":"2009","unstructured":"Reynolds DA. Gaussian mixture models. Enc Biometrics. 2009;741:659\u201363.","journal-title":"Enc Biometrics"},{"issue":"4\u20135","key":"3021_CR60","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/S0893-6080(00)00026-5","volume":"13","author":"A Hyv\u00e4rinen","year":"2000","unstructured":"Hyv\u00e4rinen A, Oja E.Independent component analysis: algorithms and applications. Neural Netw. 2000;13(4\u20135):411\u201330.","journal-title":"Neural Netw"},{"issue":"Jan","key":"3021_CR61","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei DM, Ng AY, Jordan MI.Latent dirichlet allocation. J Mach Learn Res. 2003;3(Jan):993\u20131022.","journal-title":"J Mach Learn Res"},{"key":"3021_CR62","unstructured":"Vincent P, et al. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res. 2010;11(12)."},{"issue":"1","key":"3021_CR63","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall M, et al. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter. 2009;11(1):10\u201318.","journal-title":"ACM SIGKDD Explorations Newsletter"},{"key":"3021_CR64","doi-asserted-by":"publisher","first-page":"1796","DOI":"10.1109\/JBHI.2014.2333880","volume":"18","author":"MA Estudillo-Valderrama","year":"2014","unstructured":"Estudillo-Valderrama MA, et al. A distributed approach to alarm management in chronic kidney disease. IEEE J Biomed Health Inform 2014;18:1796\u2013803.","journal-title":"IEEE J Biomed Health Inform"},{"key":"3021_CR65","unstructured":"Institute S. SAS\/STAT\u00ae online documentation, Version 9.4."},{"issue":"3","key":"3021_CR66","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1136\/jamia.2009.002733","volume":"17","author":"AR Aronson","year":"2010","unstructured":"Aronson AR, Lang F-M.An overview of MetaMap: historical perspective and recent advances. J Am Med Inf Assoc. 2010;17(3):229\u201336.","journal-title":"J Am Med Inf Assoc"},{"key":"3021_CR67","unstructured":"McCallum A, Nigam K. A comparison of event models for naive bayes text classification. In AAAI-98 workshop on learning for text categorization. 1998. Madison, WI."},{"issue":"1","key":"3021_CR68","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover T, Hart P.Nearest neighbor pattern classification. IEEE Trans Inf Theory. 1967;13(1):21\u201327.","journal-title":"IEEE Trans Inf Theory"},{"key":"3021_CR69","volume-title":"C4. 5: programs for Machine Learning","author":"JR Quinlan","year":"2014","unstructured":"Quinlan JR. C4. 5: programs for Machine Learning. Elsevier; 2014."},{"key":"3021_CR70","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1007\/978-1-4419-9863-7_1551","volume-title":"Encyclopedia of Systems Biology","author":"D Polani","year":"2013","unstructured":"Polani D, et al Kullback-Leibler Divergence. In Dubitzky W. editor Encyclopedia of Systems Biology. New York: Springer New York. 2013: 1087\u201388."},{"key":"3021_CR71","unstructured":"Ltd C. Ubuntu 14.04.6 LTS (Trusty Tahr). 2014."},{"key":"3021_CR72","unstructured":"Bates D, et al., lme4: linear mixed-effects models using Eigen and S4. R package version 1.1-7. 2014."},{"key":"3021_CR73","unstructured":"Bendix Carstensen MP, Laara E, Hills M, Epi: a package for statistical analysis in epidemiology."},{"key":"3021_CR74","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511790942","volume-title":"Data Analysis Using Regression and Multilevel\/hierarchical Models","author":"A Gelman","year":"2006","unstructured":"Gelman A, Hill J. Data Analysis Using Regression and Multilevel\/hierarchical Models. Cambridge university press; 2006."},{"key":"3021_CR75","unstructured":"Microsoft. Microsoft Excel."},{"key":"3021_CR76","unstructured":"Microsoft. Microsoft Access"},{"key":"3021_CR77","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1016\/j.jbi.2011.11.004","volume":"45","author":"JA Maldonado","year":"2012","unstructured":"Maldonado JA, et al. Using the ResearchEHR platform to facilitate the practical application of the EHR standards. J Biomed Informat. 2012;45:746\u201362.","journal-title":"J Biomed Informat"},{"key":"3021_CR78","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1016\/j.ijmedinf.2009.03.006","volume":"78","author":"JA Maldonado","year":"2009","unstructured":"Maldonado JA, et al. LinkEHR-Ed: a multi-reference model archetype editor based on formal semantics. Int J Med Inform. 2009;78:559\u201370.","journal-title":"Int J Med Inform"},{"key":"3021_CR79","unstructured":"W3C. XQuery 1.0: An XML Query Language 2010 (cited 2023 20 August); Available from: https:\/\/www.w3.org\/TR\/xquery."},{"key":"3021_CR80","unstructured":"sele.inf.um.es. Semantic Web Integration Tool (SWIT) Available from: http:\/\/sele.inf.um.es\/swit\/."},{"key":"3021_CR81","unstructured":"Saxonica. Saxon XSLT and XQuery processor. Available from: https:\/\/www.saxonica.com\/welcome\/welcome.xml."},{"key":"3021_CR82","unstructured":"Group, W.C.O.W. OWL 2 web ontology language document overview. Available from: https:\/\/www.w3.org\/TR\/owl2-overview\/."},{"issue":"4","key":"3021_CR83","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1145\/2757001.2757003","volume":"1","author":"MA Musen","year":"2015","unstructured":"Musen MA.The prot\u00e9g\u00e9 project: a look back and a look forward. AI Matters. 2015;1(4):4\u201312.","journal-title":"AI Matters"},{"key":"3021_CR84","unstructured":"Shearer RD, Motik B, Horrocks I. Hermit: a highly-efficient OWL reasoner. Owled. 2008."},{"key":"3021_CR85","unstructured":"Medicine NLO UMLS Terminology Services [cited 2023 27 August]; Available from. https:\/\/uts.nlm.nih.gov\/uts\/."},{"key":"3021_CR86","unstructured":"Foundation, o. openEHR Clinical Knowledge Manager [cited 2023 27 August]; Available from: https:\/\/ckm.openehr.org\/ckm\/."},{"key":"3021_CR87","unstructured":"International S. SNOMED Software and Tools. [cited 2023 27 August]; Available from: https:\/\/www.snomed.org\/software-tools."},{"key":"3021_CR88","unstructured":"International S. SPARQL Query Language for RDF. [cited 2023 27 August]; Available from: https:\/\/www.w3.org\/TR\/rdf-sparql-query\/."},{"key":"3021_CR89","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-017-0580-8","volume":"18","author":"R Jackson","year":"2018","unstructured":"Jackson R, et al. CogStack-experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital. BMC Med Inf Decis Making. 2018;18:1\u201313.","journal-title":"BMC Med Inf Decis Making"},{"key":"3021_CR90","unstructured":"Gorrell G, Song X, Roberts A, Bio-yodie: a named entity linking system for biomedical text. arXiv preprint arXiv:1811.04860, 2018."},{"key":"3021_CR91","unstructured":"Company ETSA Elasticsearch: the official distributed search & analytics engine. [cited 2023 27 August]."},{"key":"3021_CR92","doi-asserted-by":"crossref","unstructured":"Bodenreider O. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Research. 2004;32(suppl_1):D267\u2013D270.","DOI":"10.1093\/nar\/gkh061"},{"key":"3021_CR93","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-1-4757-3873-5_18","volume-title":"Aspects of the Computer-based Patient Record","author":"DA Lindberg","year":"1992","unstructured":"Lindberg DA, Humphreys BL. The Unified Medical Language System (UMLS) and computer-based patient records. In: Aspects of the Computer-based Patient Record, Springer, 1992:165\u201375."},{"issue":"11","key":"3021_CR94","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster M, Paliwal KK.Bidirectional recurrent neural networks. IEEE Trans Signal Process. 1997;45(11):2673\u201381.","journal-title":"IEEE Trans Signal Process"},{"key":"3021_CR95","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AE Johnson","year":"2016","unstructured":"Johnson AE, et al. MIMIC-III, a freely accessible critical care database. Sci Data 2016;3:1\u20139.","journal-title":"Sci Data"},{"key":"3021_CR96","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-244X-9-51","volume":"9","author":"R Stewart","year":"2009","unstructured":"Stewart R, et al. The South London and Maudsley NHS foundation trust biomedical research centre (SLAM BRC) case register: development and descriptive data. BMC Psychiatry. 2009;9:1\u201312.","journal-title":"BMC Psychiatry"},{"key":"3021_CR97","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1197\/jamia.M2273","volume":"14","author":"C Safran","year":"2007","unstructured":"Safran C, et al. Toward a national framework for the secondary use of health data: an American Medical Informatics Association White Paper. J Am Med Inf Assoc 2007;14:1\u20139.","journal-title":"J Am Med Inf Assoc"},{"key":"3021_CR98","first-page":"5","volume":"2","author":"P Kosseim","year":"2008","unstructured":"Kosseim P, Brady M. Policy by procrastination: secondary use of electronic health records for health research purposes. McGill JL & Health. 2008;2:5.","journal-title":"McGill JL & Health"},{"key":"3021_CR99","doi-asserted-by":"crossref","unstructured":"Tu K, et al. Are family physicians comprehensively using electronic medical records such that the data can be used for secondary purposes? A Canadian perspective. BMC Medical Informatics and Decision Making. 2015;15:1\u201312.","DOI":"10.1186\/s12911-015-0195-x"},{"key":"3021_CR100","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2288-13-92","volume":"13","author":"L Thabane","year":"2013","unstructured":"Thabane L, et al. A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. BMC Med Res Method. 2013;13:1\u201312.","journal-title":"BMC Med Res Method"},{"key":"3021_CR101","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s13222-021-00386-8","volume":"21","author":"I Prapas","year":"2021","unstructured":"Prapas I, et al. Continuous training and deployment of deep learning models. Datenbank-Spektrum 2021;21:203\u201312.","journal-title":"Datenbank-Spektrum"},{"key":"3021_CR102","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1111\/imj.13661","volume":"48","author":"S Coughlin","year":"2018","unstructured":"Coughlin S, et al. Looking to tomorrow\u2019s healthcare today: a participatory health perspective. Internal Med J 2018;48:92\u201396.","journal-title":"Internal Med J"},{"key":"3021_CR103","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1016\/j.jbi.2013.06.010","volume":"46","author":"NG Weiskopf","year":"2013","unstructured":"Weiskopf NG, et al. Defining and measuring completeness of electronic health records for secondary use. J Biomed Informat 2013;46:830\u201336.","journal-title":"J Biomed Informat"},{"key":"3021_CR104","first-page":"1","volume":"55","author":"T Sarwar","year":"2022","unstructured":"Sarwar T, et al. The secondary use of electronic health records for data mining: data characteristics and challenges. ACM Computing Surveys (CSUR) 2022;55:1\u201340.","journal-title":"ACM Computing Surveys (CSUR)"},{"issue":"2","key":"3021_CR105","doi-asserted-by":"publisher","first-page":"205395171986259","DOI":"10.1177\/2053951719862594","volume":"6","author":"J Starkbaum","year":"2019","unstructured":"Starkbaum J, Felt U.Negotiating the reuse of health-data: research, big data, and the European general data protection regulation. Big Data Soc. 2019;6(2):2053951719862594.","journal-title":"Big Data Soc"},{"issue":"3","key":"3021_CR106","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1001\/jama.2018.5630","volume":"320","author":"IG Cohen","year":"2018","unstructured":"Cohen IG, Mello MM.HIPAA and protecting health information in the 21st century. Jama. 2018;320(3):231\u201332.","journal-title":"Jama"},{"key":"3021_CR107","unstructured":"Ministry of Electronics and Information Technology, G.o.I. The digital personal data protection act, 2023. 2023."},{"key":"3021_CR108","doi-asserted-by":"publisher","first-page":"1730","DOI":"10.1093\/jamia\/ocad120","volume":"30","author":"AE Lewis","year":"2023","unstructured":"Lewis AE, et al. Electronic health record data quality assessment and tools: a systematic review. J Am Med Inf Assoc 2023;30:1730\u201340.","journal-title":"J Am Med Inf Assoc"},{"key":"3021_CR109","unstructured":"Deutschland I, PowerCenter: enterprise Data Integration Platform."},{"key":"3021_CR110","unstructured":"Tableau. Tableau: business Intelligence and Analytics Software. 2023."},{"key":"3021_CR111","unstructured":"Foundation AS. Apache Hadoop. 2023."},{"key":"3021_CR112","first-page":"1","volume-title":"Healthcare Big Data Management and Analytics in Scientific Programming","author":"S Nazir","year":"2021","unstructured":"Nazir S, et al. Healthcare Big Data Management and Analytics in Scientific Programming. vol. 2021, Scientific Programming; 2021. 1\u20132."},{"issue":"3","key":"3021_CR113","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1080\/00987913.2019.1644891","volume":"45","author":"LT Becker","year":"2019","unstructured":"Becker LT, Gould EM.Microsoft power BI: extending excel to manipulate, analyze, and visualize diverse data. Ser Rev. 2019;45(3):184\u201388.","journal-title":"Ser Rev"},{"key":"3021_CR114","doi-asserted-by":"publisher","first-page":"7329","DOI":"10.1073\/pnas.1510502113","volume":"113","author":"G Hripcsak","year":"2016","unstructured":"Hripcsak G, et al. Characterizing treatment pathways at scale using the OHDSI network. Proc Natl Acad Sci 2016;113:7329\u201336.","journal-title":"Proc Natl Acad Sci"},{"key":"3021_CR115","unstructured":"Corporation O. Oracle| integrated Cloud 0Applications and Platform Services. 2023."},{"key":"3021_CR116","unstructured":"SE S. SAP software solutions| business applications and technology. 2023."},{"key":"3021_CR117","unstructured":"Corporation I. (cited 2023 27 August). Available from: https:\/\/www.ibm.com\/us-en."},{"key":"3021_CR118","unstructured":"Engineering A. RapidMiner-Best Data Science Platform for Your Enterprise (cited 2023 27 August); Available from: https:\/\/rapidminer.com\/platform\/."},{"key":"3021_CR119","unstructured":"KNIME. KNIME Analytics Platform (cited 2023 27 August); Available from: https:\/\/www.knime.com\/knime-analytics-platform."},{"issue":"5","key":"3021_CR120","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1007\/s12553-020-00438-1","volume":"10","author":"I Tougui","year":"2020","unstructured":"Tougui I, Jilbab A, El Mhamdi J.Heart disease classification using data mining tools and machine learning techniques. Health Technol. 2020;10(5):1137\u201344.","journal-title":"Health Technol"},{"key":"3021_CR121","doi-asserted-by":"publisher","first-page":"e0145791","DOI":"10.1371\/journal.pone.0145791","volume":"11","author":"SV Poucke","year":"2016","unstructured":"Poucke SV, et al. Scalable predictive analysis in critically ill patients using a visual open data analysis platform. PloS One 2016;11:e0145791.","journal-title":"PloS One"},{"issue":"8","key":"3021_CR122","doi-asserted-by":"publisher","first-page":"4968","DOI":"10.1016\/j.jksuci.2021.06.002","volume":"34","author":"J Santos-Pereira","year":"2022","unstructured":"Santos-Pereira J, Gruenwald L, Bernardino J.Top data mining tools for the healthcare industry. J King Saud Univ, Comput Inf Sci. 2022;34(8):4968\u201382.","journal-title":"J King Saud Univ, Comput Inf Sci"},{"key":"3021_CR123","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1136\/jamia.2009.000893","volume":"17","author":"SN Murphy","year":"2010","unstructured":"Murphy SN, et al. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J Am Med Inf Assoc 2010;17:124\u201330.","journal-title":"J Am Med Inf Assoc"},{"key":"3021_CR124","unstructured":"Athey BD, et al., TranSMART: an open source and community-driven informatics and data sharing platform for clinical and translational research. AMIA Summits on Translational Science Proceedings 2013. 2013: 6."},{"issue":"1","key":"3021_CR125","first-page":"760","volume":"129","author":"T Beale","year":"2007","unstructured":"Beale T, Heard S.An ontology-based model of clinical information. Stud Health Technol Inform. 2007;129(1):760.","journal-title":"Stud Health Technol Inform"},{"key":"3021_CR126","unstructured":"build.fhir.org. FHIR v6.0.0-cibuild. (cited 2023 27 August); Available from: https:\/\/build.fhir.org."},{"key":"3021_CR127","unstructured":"International H. Health Level Seven International - HL7. (cited 2023 27 August); Available from: https:\/\/www.hl7.org\/."},{"key":"3021_CR128","unstructured":"dicomstandard.org. DICOM Standard 2019 (cited 2023 27 August); Available from: https:\/\/www.dicomstandard.org\/."},{"key":"3021_CR129","unstructured":"The National Patient-Centered Clinical Research Network (cited 2023 27 August); Available from: https:\/\/pcornet.org\/."},{"key":"3021_CR130","unstructured":"OpenAI. GPT-4 Technical Report 2023 (cited 2023 13 September); Available from: https:\/\/cdn.openai.com\/papers\/gpt-4.pdf."},{"key":"3021_CR131","unstructured":"Vaswani A, et al. Attention is all you need. Adv Neural Inf Process Syst. 2017;30."},{"key":"3021_CR132","unstructured":"Li Z, et al., Meta-sgd: learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835, 2017."},{"key":"3021_CR133","unstructured":"Balestriero R, et al., A cookbook of self-supervised learning. arXiv preprint arXiv:2304.12210, 2023."},{"key":"3021_CR134","doi-asserted-by":"publisher","first-page":"e1045","DOI":"10.7717\/peerj-cs.1045","volume":"8","author":"S Shurrab","year":"2022","unstructured":"Shurrab S, Duwairi R. Self-supervised learning methods and applications in medical imaging analysis: a survey. PeerJ Comput Sci. 2022;8:e1045.","journal-title":"PeerJ Comput Sci"},{"key":"3021_CR135","doi-asserted-by":"publisher","first-page":"2004","DOI":"10.1093\/jamia\/ocad175","volume":"30","author":"J Lemmon","year":"2023","unstructured":"Lemmon J, et al. Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks. J Am Med Inf Assoc 2023;30:2004\u201311.","journal-title":"J Am Med Inf Assoc"},{"key":"3021_CR136","doi-asserted-by":"publisher","first-page":"320","DOI":"10.3390\/jimaging8120320","volume":"8","author":"J Anton","year":"2022","unstructured":"Anton J, et al. How well do self-supervised models transfer to medical imaging? J Imaging 2022;8:320.","journal-title":"J Imaging"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03021-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-03021-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03021-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T10:04:20Z","timestamp":1747389860000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-025-03021-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,16]]},"references-count":136,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3021"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-03021-8","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,16]]},"assertion":[{"value":"29 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"190"}}