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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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Cherry, C. A., Zhu, X., Martin, J., & de Bruijn, B. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001624 · Cited by 34
Collaborative search in electronic health records
Zheng, K., Mei, Q., & Hanauer, D. A. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000009 · Cited by 34
Hurdle, J. F., Haroldsen, S. C., Hammer, A., Spigle, C., Fraser, A. M., Mineau, G. P., & Courdy, S. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001050 · Cited by 33
Use of electronic health record data to evaluate overuse of cervical cancer screening
Mathias, J. S., Gossett, D. R., & Baker, D. W. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000536 · Cited by 33
Haynes, K., Linkin, D. R., Fishman, N. O., Bilker, W. B., Strom, B. L., Pifer, E. A., & Hennessy, S. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.002998 · Cited by 33
An adaptable architecture for patient cohort identification from diverse data sources
Bache, R., Miles, S., & Taweel, A. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001858 · Cited by 30
Jonnalagadda, S., Li, D., Sohn, S., Wu, S. T.-I., Wagholikar, K. B., Torii, M., & Liu, H. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000766 · Cited by 28
Exploiting domain information for Word Sense Disambiguation of medical documents
Stevenson, M., Agirre, E., & Soroa, A. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000415 · Cited by 28
Klann, J. G., Anand, V., & Downs, S. M. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001865 · Cited by 27
Automatic discourse connective detection in biomedical text
Ramesh, B. P., Prasad, R., Miller, T., Harrington, B., & Yu, H. Q. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000775 · Cited by 26
Warner, J. L., Zollanvari, A., Ding, Q., Zhang, P., Snyder, G. M., & Alterovitz, G. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001861 · Cited by 26
A classification approach to coreference in discharge summaries: 2011 i2b2 challenge
Xu, Y. L., Liu, J., Wu, J., Wang, Y., Zhuowen Tu, A., Sun, J.-T., Tsujii, J., & Chang, E. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000734 · Cited by 25
Austrian, J. S., Adelman, J. S., Reissman, S. H., Cohen, H. W., & Billett, H. H. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000138 · Cited by 22
Li, Q., Zhai, H., Deléger, L., Lingren, T., Kaiser, M., Stoutenborough, L., & Solti, I. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001487 · Cited by 21
London, J., Balestrucci, L., Chatterjee, D., & Zhan, T. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001846 · Cited by 20
The feasibility of automating audit and feedback for ART guideline adherence in Malawi
Lewis, Z. L., Mello-Thoms, C. R., Gadabu, O. J., Gillespie, E. M., Douglas, G. P., & Crowley, R. S. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000097 · Cited by 16
Detecting pregnancy use of non-hormonal category X medications in electronic medical records
Strom, B. L., Schinnar, R., Jones, J., Bilker, W. B., Weiner, M. G., Hennessy, S., Leonard, C. E., Cronholm, P. F., & Pifer, E. A. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2010-000057 · Cited by 15
Improving patient safety via automated laboratory-based adverse event grading
Niland, J. C., Stiller, T., Neat, J., Londrc, A., Johnson, D., & Pannoni, S. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000513 · Cited by 13
A supervised framework for resolving coreference in clinical records
Rink, B., Roberts, K. E., & Harabagiu, S. M. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-000810 · Cited by 13
Carroll, N. M., Ellis, J. L., Luckett, C. F., & Raebel, M. A. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000151 · Cited by 13
Dai, H.-J., Chen, C.-Y., Wu, C.-Y., Lai, P., Tsai, R. T., & Hsu, W.-L. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-000808 · Cited by 13
Machine learning-based coreference resolution of concepts in clinical documents
Ware, H., Mullett, C. J., Jagannathan, V., & El-Rawas, O. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000774 · Cited by 11
MCORES: a system for noun phrase coreference resolution for clinical records
Bodnari, A., Szolovits, P., & Uzuner, Ö. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000591 · Cited by 10
Presence of key findings in the medical record prior to a documented high-risk diagnosis
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Rosenbloom, S. T., Madison, J. L., Brothers, K. B., Bowton, E., Clayton, E. W., Malin, B., Roden, D. M., & Pulley, J. M. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001937 · Cited by 8
A rule based solution to co-reference resolution in clinical text
Chen, P., Hinote, D., & Chen, G. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000770 · Cited by 6
Himes, B. E., Klanderman, B. J., Kohane, I. S., & Weiss, S. T. (2011) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2010.12.007 · Cited by 6
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Human symptoms–disease network
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Mining Electronic Health Records (EHRs): A Survey
Yadav, P., Steinbach, M., Kumar, V., & Simon, G. (2018) · ACM Computing Surveys · doi:10.1145/3127881 · Cited by 299
“In the realm of supervised temporal pattern mining, research has extended the temporal abstraction framework by mining recent temporal patterns for monitoring and event detection problems in patients suffering from T2DM (Batal et al. 2012).”
BackgroundMethodInfluentialA critical assessment of using ChatGPT for extracting structured data from clinical notes
Huang, J., Yang, D. M., Rong, R., Nezafati, K., Treager, C., Chi, Z., Wang, S., Cheng, X., Guo, Y., Klesse, L. J., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01079-8 · Cited by 298
Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models
Banda, J. M., Seneviratne, M. G., Hernandez‐Boussard, T., & Shah, N. H. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013315 · Cited by 228
“The widespread adoption of electronic health records (EHRs) over the past decade in the United States has generated vast repositories of clinical data (1).”
BackgroundInfluentialAutomated methods for the summarization of electronic health records
Perotte, R., & Elhadad, N. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv032 · Cited by 227
“This knowledge representation effort has been successful in helping efforts like phenotyping combine terminological knowledge, expert reasoning, and machine learning to create actionable disease definitions.(102) Similarly in summarization work, it is important to make use of these available clinical knowledge represe…”
Method
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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