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Electronic health records-driven phenotyping: challenges, recent advances, and perspectives
Open accessAbstract
With the completion of the Human Genome Project1 as well as recent advances in genomic science and comparative biological studies, a new era of individualized medicine is evolving where novel biomedical discoveries are leading to more effective prevention, treatment, and diagnosis of disease. Although altered phenotypes are one of the most reliable manifestations of altered gene functions, research in extracting, representing, and analyzing phenotype–genotype relationships is still evolving. This has led to the emergence of a trans-discipline field, called ‘Phenomics,’2 that aims to capitalize on high-throughput computation and informatics technologies for the systematic study of phenotypes and how they might influence personal genomics.3 Many comparative phenomics studies in the recent past4,5 have demonstrated the power of positively correlating phenotypes with several measures of gene functions. However, despite the advances, research in phenomics is presented with various challenges, including (i) developing approaches for high-throughput extraction and representation of phenotypes, (ii) building techniques for storing, integrating, and querying phenotype data, and (iii) advancing phenotypic-driven analysis to derive phenotype–genotype associations. A significant barrier in the discovery of new genetic variants is the requirement to obtain the large sample sizes needed for an effective study (since variants may be rare within a population) leading to time-consuming and onerous sample collection efforts. Electronic health records (EHRs) can accelerate clinical research and genomic medicine, but are hindered by the limited number of validated processes and tools to enable accurate and rapid phenotype extraction.6 EHRs are increasing in ubiquity, functionality, and comprehensiveness across the USA, in part due to Meaningful Use standards7 implemented as part of the Health Information Technology for Economic and Clinical Health (HITECH) Act. One recent advance has been the coupling of DNA biorepositories to EHR data,8–13 combined with advances in informatics techniques, such as natural language processing (NLP), to enable genomic discoveries. The Electronic Medical Records and Genomics (eMERGE14,15) consortium—a network of nine academic medical centers—has demonstrated the effectiveness of EHR-derived phenotyping algorithms for cohort identification to conduct genome- and phenome-wide association studies.16–20 Algorithms to define phenotypes in eMERGE have typically followed an iterative path, and highlight the importance of intermittent chart review to validate phenotype accuracy, typically at more than one site (when a multisite implementation is planned16). Once finalized, eMERGE phenotype algorithms (as well as non-eMERGE algorithms) can be viewed at http://phekb.org. While eMERGE presents an exciting and encouraging demonstration of secondary use of EHR data, evaluating the strengths and limitations for EHRs has important implications for clinical and translational research, including clinical trials, observational cohorts, outcomes research, and comparative effectiveness research. These issues are further amplified within the realm of a learning healthcare system21 that emphasizes the ability to have real-time access to knowledge, digital capture of care experience, engaged and empowered patients, alignment of incentives to value, and a leadership-instilled culture of learning (figure 1). Hripcsak and Albers6 highlight several such challenges in leveraging EHRs for research, including data that is often incomplete, inaccurate, highly complex, and biased, and propose a combination of top-down knowledge engineering and bottom-up learning and analysis to address these issues (figure 2). Specifically, understanding the inherent complexity involved in a healthcare process model is critically important towards achieving a scalable and high-throughput phenotyping process. Different phases in a learning healthcare system (figure adapted with permission from Greene et al21). Electronic health record (EHR)-driven phenotyping and knowledge discovery (figure adapted with permission from Hripcsak and Albers6). Several have acknowledged that there is a recognized tension between facilitating data entry by allowing narrative text in EHRs, and utilizing these data for research.22 A key component for identifying patient cohorts in the EHR is to define inclusion and exclusion criteria that algorithmically select sets of patients based on stored clinical data. This process allows the definition of phenotypes over structured data (eg, demographics, diagnoses, medications, and laboratory measurements)23 as well as unstructured clinical text (eg, radiology reports,22,24 encounter notes, and discharge summaries25–30). In general, this process can be quite complex, involving heuristics encoded as rules or machine learning algorithms.31,32 Several NLP techniques33 have been developed specifically for clinical text, and includ
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“Advancements in automated detection of SI, SB, and SA from clinical notes have shown varied success17,18,19,20,21,22,23,24,25,26.”
From Deep learning for identifying personal and family history of suicidal thoughts and behaviors from EHRs (2024) · cites this paper as [19]
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“These approaches, ranging from rule-based algorithms to ML techniques, primarily focus on identifying suicidal tendencies without considering their temporal aspects, often conflating current and historical events.”
From Deep learning for identifying personal and family history of suicidal thoughts and behaviors from EHRs (2024) · cites this paper as [19]
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“Traditionally, the automated extraction of structured data elements from medical notes has relied on medical natural language processing (NLP) using rule-based or machine-learning approaches or a combination of both9,10.”
From A critical assessment of using ChatGPT for extracting structured data from clinical notes (2024) · cites this paper as [10]
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Citations
Cites
The “Meaningful Use” Regulation for Electronic Health Records
Blumenthal, D., & Tavenner, M. (2010) · New England Journal of Medicine · doi:10.1056/nejmp1006114 · Cited by 2,059
Development of a Large-Scale De-Identified DNA Biobank to Enable Personalized Medicine
Roden, D. M., Pulley, J. M., Basford, M., Bernard, G. R., Clayton, E. W., Balser, J., & Masys, D. R. (2008) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2008.89 · Cited by 966
Next-generation phenotyping of electronic health records
Hripcsak, G., & Albers, D. J. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001145 · Cited by 795
the eMERGE Team, McCarty, C. A., Chisholm, R. L., Chute, C. G., Kullo, I. J., Jarvik, G. P., Larson, E. B., Li, R., Masys, D. R., Ritchie, M. D., et al. (2011) · BMC Medical Genomics · doi:10.1186/1755-8794-4-13 · Cited by 701
The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future
Gottesman, O., Kuivaniemi, H., Tromp, G., Faucett, W. A., Li, R., Manolio, T. A., Sanderson, S. C., Kannry, J. L., Zinberg, R. E., Basford, M., et al. (2013) · Genetics in Medicine · doi:10.1038/gim.2013.72 · Cited by 691
Self-citationMiddleton, B., Bloomrosen, M., Dente, M. A., Hashmat, B., Koppel, R., Overhage, J. M., Payne, T. H., Rosenbloom, S. T., Weaver, C., & Zhang, J. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001458 · Cited by 618
Validation of a common data model for active safety surveillance research
Overhage, J. M., Ryan, P. B., Reich, C. G., Hartzema, A. G., & Stang, P. E. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000376 · Cited by 548
The Human Genome Project: Past, Present, and Future
Watson, J. D. (1990) · Science · doi:10.1126/science.2181665 · Cited by 537
MedEx: a medication information extraction system for clinical narratives
Xu, H., Stenner, S. P., Doan, S., Johnson, K. B., Waitman, L. R., & Denny, J. C. (2010) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m3378 · Cited by 532
Self-citationEvaluating temporal relations in clinical text: 2012 i2b2 Challenge
Sun, W., Rumshisky, A., & Uzuner, Ö. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001628 · Cited by 497
Research works printed but not linked: 1
- Wolf W Doyle M Aufox S . DNA banking study in an ethnically diverse urban university hospital. Am J Hum Genet 2003;73:423.
Cited by
Sorted by citations
Items 201–272 of 272
KOSCIELNIAK, N. J., Piatt, G., Friedman, C. P., Vinson, A. H., Richesson, R., & Tucker, C. A. (2021) · Learning Health Systems · doi:10.1002/lrh2.10266 · Cited by 2
“Evidence demonstrates that the re-use of EHR data improves patient cohort identification and may be essential to support pragmatic prospective cohort studies with the economy of scale.(6-9,41-44) However, the discrepancy between derived definitions and the performance and use of phenotypes in practice points to a need…”
Utilizing Electronic Dental Record Data to Track Periodontal Disease Change
Patel, J. S. (2020) · IUScholarWorks (Indiana University) · doi:10.7912/c2/958 · Cited by 3
Guggilla, V., Kang, M., Bak, M. J., Tran, S., Pawlowski, A. E., Nannapaneni, P., Rasmussen, L. V., Schneider, D., Donnelly, H. K., Agrawal, A., et al. (2025) · medRxiv · doi:10.1101/2025.01.16.25320564 · Cited by 3
CLINES: Clinical LLM-based Information Extraction and Structuring Agent
Yang, Z., Yuan, H., Sayeed, R., Tan, A. L. M., Cai, E., Moro, M., Li, X., Ying, H., Brown, N., Weber, G. M., et al. (2026) · BMJ Digital Health & AI · doi:10.64898/2025.12.01.25341355 · Cited by 3
Identifying postpartum depression subtypes using natural language processing and clinical notes
Adekkanattu, P. M., Vekaria, V., Zhang, Y., Patra, B. G., Liang, P., Sharko, M., Benda, N. C., Turchioe, M. R., Temkin, A. B., Hermann, A. D., et al. (2026) · BMJ Mental Health · doi:10.1136/bmjment-2025-302066 · Cited by 2
“EHRs are widely used for phenotyping and developing risk prediction models, 12 and several studies, including our own, have explored PPD detection using structured EHR data.”
Self-citationCP Tensor Decomposition with Cannot-Link Intermode Constraints
Henderson, J., Malin, B., Denny, J. C., Kho, A., Sun, J., Ghosh, J., & Ho, J. C. (2019) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611975673.80 · Cited by 2
“For example, the CP factorization of EHR tensors can help researchers identify patients for various studies [3, 4, 5].”
Self-citationXu, H., & Roberts, K. E. (2022) · Cognitive informatics in biomedicine and healthcare · doi:10.1007/978-3-031-09108-7_7 · Cited by 2
Lake, J. (2019) · Springer eBooks · doi:10.1007/978-3-030-15285-7_6 · Cited by 2
Tan, Z., Tomaszewski, J., Chen, B. P.-H., Gunja, N. J., & Etter, K. (2024) · Journal of Comparative Effectiveness Research · doi:10.57264/cer-2023-0110 · Cited by 2
“EMRs also provide efficient access to detailed, longitudinal data that would otherwise be difficult to collect [39 , 40] .”
BackgroundPatient Record Summarization Through Joint Phenotype Learning and Interactive Visualization
Levy-Fix, G. (2020) · AMIA · doi:10.7916/d8-hba0-nx88 · Cited by 2
Beskow, L. M., Brelsford, K. M., & Hammack‐Aviran, C. M. (2020) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.524 · Cited by 2
“, genomic information) [21,24] – and to assess the outcomes of alternative policies in actual practice.”
BackgroundPIVETed-Granite: Computational Phenotypes through Constrained Tensor FactorizationPreprint
Henderson, J., Malin, B., Ho, J. C., & Ghosh, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.02602 · Cited by 2
“ontact the owner/author(s). MLMH’18, August 2018, London, United Kingdom © 2018 Copyright held by the owner/author(s). ACM ISBN 978-x-xxxx-xxxx-x/YY/MM. https://doi.org/10.1145/nnnnnnn.nnnnnnn (EHRs) [10]. For a set of computational phenotypes to be useful to clinicians, they should be 1) sparse (i.e., have relatively…”
BackgroundInfluentialYu, J., Pacheco, J. A., Ghosh, A., Luo, Y., Weng, C., Shang, N., Benoit, B., Carrell, D., Carroll, R., Dikilitas, O., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-723873/v1 · Cited by 2
Loo, S. V., & North, F. (2016) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-016-0279-2 · Cited by 1
“to generate a patient phenotype [7–11].”
Hekman, D. J., Maru, A., Barton, H. J., Wiegmann, D. A., Shah, M. N., Cochran, A. L., Ötleş, E., & Patterson, B. W. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf047 · Cited by 1
Bhojani, N., Morris, K., White, J., Rojanasarot, S., Tran, E. D., & Monga, M. (2025) · Expert Review of Medical Devices · doi:10.1080/17434440.2025.2557403 · Cited by 2
“EHRs also provide efficient access to detailed, longitudinal data that would otherwise be difficult to collect [38,39].”
Murphy, A. (2026) · Disability & Society · doi:10.1080/09687599.2026.2709522 · Cited by 2
Phenome-Wide Association Study
Warner, J. L., & Denny, J. C. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-801577-3.00003-4 · Cited by 1
Self-citationPhenotype Inference with Semi-Supervised Mixed Membership Models
Rodriguez, V., & Perotte, A. (2018) · PubMed · doi:10.48550/arxiv.1812.03222 · Cited by 1
“By answering the question of who has what disease, phenotypes power essential tasks such as cohort selection, trial recruitment and clinical outcome prediction [1–4].”
Testing calibration of phenotyping models using positive-only electronic health record data
Zhang, L., Yanyuan Ma, B., Herman, D. S., & Chen, J. (2021) · Biostatistics · doi:10.1093/biostatistics/kxab003 · Cited by 1
A latent topic model for mining heterogenous non-randomly missing electronic health records dataPreprint
Li, Y., & Kellis, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00464 · Cited by 1
Cava, W. L., Lee, P. C., Ajmal, I., Ding, X., Solanki, P., Cohen, J. B., Moore, J. H., & Herman, D. S. (2020) · medRxiv · doi:10.1101/2020.12.12.20248005 · Cited by 1
“The adoption of electronic health records (EHRs) is transforming medicine by aiding clinical decision making and facilitating translational research.(1,2) In order to leverage EHR data, researchers must often define rules or algorithms known as computable phenotypes that transparently identify patient cohorts with cer…”
BackgroundLee, K., Mai, Y., Liu, Z., Raja, K., Jun, T. W., Ma, M., Wang, T., Ai, L., Calay, E. S., Oh, W. K., et al. (2024) · medRxiv · doi:10.1101/2024.02.28.24303396 · Cited by 1
“However, automated clinical phenotyping from EHR data presents considerable challenges 14 .”
BackgroundElectronic Health Records in the Genomics Era: Impact of Clinical Genomics
Narayanan, R. (2015) · MOJ Proteomics & Bioinformatics · doi:10.15406/mojpb.2015.02.00045
“The Genome-Wide Association Studies (GWAS) are beginning to provide valuable clues to risk of disease development and phenotypic association for diverse diseases [13-19].”
BackgroundDisseminating informatics knowledge and training the next generation of leaders
Lucila Ohno-machado, A. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2014-noveditorial
Pan, X., & Cimino, J. J. (2014) · PubMed Central
Computational Disease Subtyping based on Joint Analysis of Clinical and Genomic DataPreprint
Diaz, D., Bollig-Fischer, A. B., & Kotov, A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/307751
MKDS: A Medical Knowledge Discovery System Learned from Electronic Medical Records (Demonstration)
Huang, H., Yen, A.-Z., & Chen, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03520-4_19
“EMRs are used in many applications including assertion classification [6], clinical trait extraction [2], co-reference resolution [14], temporal analysis [13], pneumonia identification [1], phenotyping [4, 10, 11], and outpatient department recommendation [5].”
MethodZhang, L., Ding, X., Yanyuan Ma, B., Muthu, N., Ajmal, I., Moore, J. H., Herman, D. S., & Chen, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.10060
Shung, D. L., Tsay, C. J., Laine, L., Thomas, P. G., Partridge, C. M., Simonov, M., Hsiao, A., & Taylor, A. (2020) · medRxiv · doi:10.1101/2020.07.06.20136374
“Phenotyping is typically the first step in developing and validating risk stratification models within EHRs.(5) Such processes have been used to improve the accuracy of case definition of inflammatory bowel disease patients as Crohn’s disease or ulcerative colitis, as well as to facilitate clinical trial recruitment a…”
MethodComputational phenotyping of obstructive airway diseases: protocol for a systematic reviewPreprint
Bashir, M. B. A., Basna, R., Zhang, G., Beckman, H., Lindberg, A., Ekerljung, L., Axelsson, M., Hedman, L., Vanfleteren, L. E. G. W., Lundbäck, B., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-56360/v2
KOSCIELNIAK, N. J. (2020) · Deep Blue (University of Michigan)
Fairfield, C. J., Cambridge, W. A., Cullen, L., Drake, T. M., Knight, S. R., Masson, N., Mills, N. L., Pius, R., Shaw, C. A., Wu, H., et al. (2021) · medRxiv · doi:10.1101/2021.10.06.21264629
Discovery of Type 2 Diabetes Trajectories from Electronic Health Records
Oh, W.-S. (2020) · University of Minnesota Digital Conservancy (University of Minnesota)
Shepherd, B. E., Han, K., Chen, T., Bian, A., Pugh, S., Duda, S. N., Lumley, T., Heerman, W. J., & Shaw, P. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.14001
Study Design for Genetic Studies
Crawford, D. C., & Dumitrescu, L. C. (2021) · Wiley eBooks · doi:10.1002/9781119104100.ch4
Luo, M., & Yang, X. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06869-9
“Previous work mostly relies on expert-defined phenotyping [3, 4] or manual feature engineering [5], which are often labor consuming.”
MethodComputational Phenotyping of Obstructive Airway Diseases: Protocol for A Systematic ReviewPreprint
Bashir, M. B. A., Basna, R., Zhang, G., Beckman, H., Lindberg, A., Ekerljung, L., Axelsson, M., Hedman, L., Vanfleteren, L. E. G. W., Lundbäck, B., et al. (2020) · Research Square · doi:10.21203/rs.3.rs-56360/v1
Schulz, S. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.736596838.793567597
Characterizing Variability of EHR-Driven Phenotype DefinitionsPreprint
Brandt, P., Kho, A., Luo, Y., Pacheco, J. A., Walunas, T. L., Hakonarson, H. H., Hripcsak, G. M., Liu, C., Shang, N., Weng, C., et al. (2022) · medRxiv · doi:10.1101/2022.07.10.22277390
Self-citationEdgcomb, J. B., Tseng, C., Pan, M., Klomhaus, A. M., & Zima, B. T. (2023) · PubMed Central
Dai, G., & Chen, J. (2023) · Statistica Sinica · doi:10.5705/ss.202023.0373
HERALD: A domain-specific query language for longitudinal health data analytics
Baum, L., Johns, M., Müller, A., Attieh, H. A., & Praßer, F. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105646
Chen, Y., Sun, J., Dong, M., Meng, Z., Yang, Y., Muhetaier, A., Li, C., & Qin, J. (2025) · doi:10.2196/preprints.70967
Severson, E. P., Tan, Z., Kabiri, M., English, A., Reimer, J., Goldstein, L., & Etter, K. (2025) · Research Square · doi:10.21203/rs.3.rs-6925135/v1
Kumar, P., Viszolay, A. D., Upadhayaya, R., Moomtaheen, F., Greer, D. R., Bologa, C., Schneider, K. A., Davis, S. E., Matheny, M. E., van der Goes, D. N., et al. (2025) · doi:10.2196/preprints.89071
Hammam, N., Li, J., Evans, M., Kay, J. L., Izadi, Z., Anastasiou, C., Gianfrancesco, M. A., Yazdany, J., & Schmajuk, G. (2021) · Figshare · doi:10.6084/m9.figshare.16544281
Dong, H., Suárez-Paniagua, V., Zhang, H., Wang, M., Casey, A., Davidson, E. M., Chen, J., Alex, B., Whiteley, W., & Wu, H. (2024) · Figshare · doi:10.6084/m9.figshare.26587488
Binkheder, S., Wu, H., Quinney, S. K., Zhang, S., Zitu, M. M., Chiang, C., Wang, L., Jones, J., & Li, L. (2022) · Figshare · doi:10.6084/m9.figshare.20056538
Kothari, C. R., Srivastava, S., Kousa, Y., Izem, R., Gierdalski, M., Kim, D., Good, A. B., Dies, K. A., Geisel, G., Morizono, H., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19408208
Kothari, C. R., Srivastava, S., Kousa, Y., Izem, R., Gierdalski, M., Kim, D., Good, A. B., Dies, K. A., Geisel, G., Morizono, H., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19408536
Dong, H., Suárez-Paniagua, V., Zhang, H., Wang, M., Casey, A., Davidson, E. M., Chen, J., Alex, B., Whiteley, W., & Wu, H. (2024) · Figshare · doi:10.6084/m9.figshare.26587485
Yu, J., Pacheco, J. A., Ghosh, A. S., Luo, Y., Weng, C., Shang, N., Benoit, B., Carrell, D., Carroll, R. J., Dikilitas, O., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19091009
Bashir, M. B. A., Basna, R., Zhang, G.-Q., Backman, H., Lindberg, A., Ekerljung, L., Axelsson, M., Hedman, L., Vanfleteren, L. E. G. W., Lundbäck, B., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21332890
DeepSelective: Interpretable prognosis prediction via feature selection and compression in EHR data
Zhang, R., Yang, Q., Wang, X., Wang, T., Zhou, Q., Deng, Z., Li, K., Wang, Y., Fan, Y., Zhang, J., et al. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112970 · Cited by 3
Model calibration and evaluation via optimal subsampling using electronic health record data
Lee, J., Dai, G., Rader, D. J., & Chen, J. (2026) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1093/jrsssa/qnag036
Kumar, P., Viszolay, A. D., Upadhayaya, R., Moomtaheen, F., Greer, D. R., Bologa, C., Schneider, K. A., Davis, S. E., Matheny, M. E., van der Goes, D. N., et al. (2026) · Journal of Medical Internet Research · doi:10.2196/89071
Identifying a Refractory Shock Phenotype in Pediatric Sepsis Using a Vasoactive-Adjusted Shock Index
Lee, C. M., Atreya, M. R., Bennett, T. D., Farris, R. W. D., Faustino, E. V. S., Geva, A., Rogerson, C. M., Shah, S., Weiss, S. L., & Sanchez-Pinto, L. N. (2026) · Shock · doi:10.1097/shk.0000000000002886
Development of a Digital Phenotype for Immune-Related Colitis
Teng, J. C., Worth, L. J., Thursky, K., Trinh, A., Spain, L., Tran, P. K., Spelman, T. D., Nkonde, C., Terreros-Martin, S., Roubos, G., et al. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00366
Martin, S. S., Grauman, Å., Coulter, J., Hasan, D. B. K., Veldwijk, J., Hansson, M., Anyouzoa, A., Nyoungui, E. F., Elomaa, K., & Zschüntzsch, J. (2026) · Healthcare · doi:10.3390/healthcare14142153
Kyriakou, S., Georghiou, G. P., Georghiou, P., Kyriakou, A., Georgiou, A., Neokleous, M., Xanthopoulos, A. V., & Triposkiadis, F. (2026) · Medicina · doi:10.3390/medicina62081597
“Confirmed findings may prompt reassessment of anticoagulation, bleeding risk, symptoms, and rate or rhythm control; they should not automatically determine these decisions or the scheduling of oncology treatment [38,39].”
Purohit, M., Ioachimescu, O. C., & Madiraju, P. (2026) · IEEE International Conference on AI and Data Analytics (ICAD) · doi:10.1109/icad69378.2026.11608983
“Regarding ICD-code labeling limitations: while ICD-10 coding provides a scalable mechanism for EHR phenotyping [21], it is subject to coding variability across providers, potential underascertainment in patients without dedicated sleep study referrals, and diagnostic lag.”
Gustafson, C. M., Kazmerski, T. M., & Koleck, T. A. (2026) · Nursing Outlook · doi:10.1016/j.outlook.2026.102854
Beger, C., Strobach, D., Schäfermeier, R., Matthies, F., Höffner, K., & Uciteli, A. (2026) · Journal of Biomedical Semantics · doi:10.1186/s13326-026-00364-7
“Within the realm of phenotyping, a phenotype algorithm is commonly understood as an algorithm intended to identify or classify phenotypic traits [39, 40].”
Huo, Y., Deng, G., Li, K., Qin, H., Xiao, L., Deng, Y., Chen, X., Ding, D., Wang, F., Zhang, M., et al. (2026) · JMIR Medical Informatics · doi:10.2196/100063
“Electronic health record phenotyping has made substantial progress in deriving computable disease definitions from structured and unstructured clinical data [1].”
Hierarchical multi-label structuring of Japanese SOAP clinical notes with large language models
Zhang, Z., Tang, M., Han, F., Kitai, H., Hirata, K., Ogasawara, K., Konno, S., & Kudo, K. (2026) · Scientific Reports · doi:10.1038/s41598-026-58622-8
Gasperi, M., Gerstenberger, A., Wild, M. G., Stein, M. B., & Afari, N. (2026) · Journal of Pain · doi:10.1016/j.jpain.2026.106435
Beyond Pain Intensity: A Patient-Informed Framework for Documenting Burden in Small Fiber Neuropathy
Hochman, G. (2026) · Neurology and Therapy · doi:10.1007/s40120-026-01063-2
Metadata
- Authors
- Pathak, J., Kho, A. N., & Denny, J. C.
- Affiliations
- Mayo Clinic (US)
- Vanderbilt University (US)
- Corresponding author
- Pathak, Jyotishman
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-12
- Volume
- 20
- Issue
- e2
- Pages
- e206-e211
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC3861925
- OpenAlex
- W2151068025
- Cited by
- 274
- References
- 108
- Citations by year
- 2026: 16 · 2025: 16 · 2024: 18 · 2023: 18 · 2022: 24 · 2021: 22 · 2020: 34 · 2019: 28 · 2018: 24 · 2017: 18 · 2016: 21 · 2015: 23 · 2014: 12
- FWCI
- 32.21
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (U19HL065962)
- National Human Genome Research Institute (U01HG006379, U01HG006378, U01HG006388)
- National Institute of General Medical Sciences (R01GM105688, U19GM061388)
- U.S. National Library of Medicine (R01LM010685)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Keywords
- electronic health records, phenotype extraction, genotype-phenotype correlation, natural language processing, cohort identification, rare variants, genomic medicine, meaningful use, HITECH Act, phenotype validation, clinical research informatics, phenomics, genome-wide association study, phenome-wide association study
- MeSH terms
- Academic Medical Centers, Algorithms, Humans, Phenotype, Genomics, Genetic Association Studies, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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