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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]
Experimental — accuracy not yet measuredIn the citing paper
“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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“However, there is currently a mismatch wherein the ability to extract accurate information about patient phenotypes from EHRs lags behind genotyping.(2) For example, although EHRs often contain diagnosis codes for specific diseases, the presence or absence of these codes is not perfectly correlated with the presence o…”
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“Compared to genotype data, phenotype data from sources such as EHRs are more complex, and pose challenges with respect to accuracy, harmonization and standard development (Pathak et al., 2013).”
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“In this context, computational problems related to personalized medicine can be broken into two broad categories: forecasting, where we make quantitative predictions about a patient’s future state that can be used by clinicians and patients to take corrective action, and phenotyping ([5, 6, 7, 8, 9]), where we identif…”
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“This can be achieved by enhancing existing risk scoring systems such as the LACE (Length of stay, Acuity of the admission, Comorbidity of the patient, and emergency department use in the duration of 6 months before admission) index(32) and the Framingham Risk Score(33) for improving patient outcomes(34) and supporting…”
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“The widespread adoption of an electronic health record (EHR) has generated large amounts of data, providing an opportunity for clinical phenotyping to identify patients with characteristics of interest [1,2].”
Backgroundvan Os, H. J. A., Kanning, J. P., Wermer, M. J. H., Chavannes, N. H., Numans, M. E., Ruigrok, Y. M., van Zwet, E. W., Putter, H., Steyerberg, E. W., & Groenwold, R. H. H. (2022) · Frontiers in Epidemiology · doi:10.3389/fepid.2022.871630 · Cited by 12
“It should also be noted that for many EHR-derived diagnoses, available reference standards may a certain degree of misclassification (32).”
BackgroundOse, D. J., Adediran, E., Owens, R., Gardner, E., Mervis, M., Turner, C., Carlson, E., Forbes, D., Jasumback, C. L., Stuligross, J., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/42409 · Cited by 13
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A multi-institution evaluation of clinical profile anonymization
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Graph Kernel Prediction of Drug Prescription
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“For example, electronic phenotyping is the process of extracting relevant features from EHRs, a major step before performing an analytical task [8].”
BackgroundTASKA: A modular task management system to support health research studies
Almeida, J. R., Gini, R., Roberto, G., Rijnbeek, P. R., & Oliveira, J. L. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0844-6 · Cited by 10
“Data source-tailored case-finding algorithms need to be identified, tested and chosen [20] through an expedited procedure [21].”
Bornet, A., Proios, D., Yazdani, A., Jaume-Santero, F., Haller, G., Choi, E., & Teodoro, D. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103108 · Cited by 11
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“Accordingly, computational phenotyping is a clinical data science method that leverages data-driven methods to subtype and characterize patient conditions from heterogeneous EHR data, 5 and yield phenotyping algorithms that could be applied across datasets.”
MethodA simplified retriever to improve accuracy of phenotype normalizations by large language models
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“Manual phenotyping is labor intensive, which makes high-throughput automated methods essential [11, 14, 15, 16, 17, 18].”
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“Identifying disease phenotypes using EHR data has been a growing area of interest with the rapid increase in EHR adoption nationally [12, 13].”
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“However, extracting phenotype information from EHRs is not an easy task [4] due to the data fragmentation, multi-modality, and lack of uniform inclusion criteria.”
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“Benefits of EMR use for research studies The vast amounts of clinical data stored in EMRs present a potential source of data for research studies [6,7].”
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“In order to address these challenges, before making hospital readmission predictions, we should translate raw healthcare data into clinically relevant electronic phenotypes, exist a number of proposals for is basically a feature extraction process [4], [5].”
BackgroundRule-based and Machine Learning Hybrid System for Patient Cohort Selection
Antunes, R., Silva, J., Pereira, A., & Matos, S. (2019) · International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) · doi:10.5220/0007349300590067 · Cited by 9
“It is due to this widely recognized potential that much research has focused on levering unstructured data from EHRs, using for that purpose techniques such as NLP (Natural Language Processing) to process unstructured text and extract meaningful content (Pathak et al., 2013).”
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Multiple Graph Kernel Fusion Prediction of Drug Prescription
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“3342134 Electronic Health Recoreds (EHRs), providing opportunities to mine medical data [1, 29].”
BackgroundOzrazgat‐Baslanti, T., Motaei, A., Islam, R., Hashemighouchani, H., Ruppert, M. M., Madushani, R. W. M. A., Segal, M. S., Lipori, G. P., Bihorac, A., & Hobson, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03149 · Cited by 12
“These queries can be designed to identify patients with a particular condition and, ideally, to clinically stage those conditions to support observational and interventional research.(4,5) Techniques used include a combination of data mining, natural language processing techniques and regression analyses.”
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“Despite the potential benefits, automated clinical phenotyping from EHR data faces several challenges 16 .”
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“The EMR data also enabled efficient and accurate longitudinal data collection for patients [35, 36].”
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“EMRs also provide efficient access to detailed, longitudinal data that would otherwise be difficult to collect 27,28 .”
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“In the remainder of this section, we discuss how principles underlying the Causal Roadmap can guide the use of machine learning to identify an outcome from data – a classification activity known as “ phenotyping ” in the clinical informatics literature [30].”
Advances in translational biomedicine from systems approaches
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“Next-Gen technologies and Electronic Medical/Health Records are providing researchers with data resources correctly classified as “Big Data” (Pathak et al., 2013).”
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“38 Finally, while we were able to uncover interesting trends in asthma-associated healthcare utilization in minority groups in this version of the ADP, we could not explore this relationship further due to the absence of additional potentially relevant characteristics inclusive of indicators of…”
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“To obtain corresponding prescription, lab, and procedure codes, we then reviewed clinical practice guidelines for the identified conditions (eg, stroke, chronic obstructive pulmonary disease) from the United States Preventive Services Task Force [18,22].”
An updated, computable MEDication-Indication resource for biomedical research
Zheng, N. S., Kerchberger, V. E., Borza, V. A., Eken, H. N., Smith, J., & Wei, W. (2021) · Scientific Reports · doi:10.1038/s41598-021-98579-4 · Cited by 7
Kukhareva, P., Staes, C. J., Noonan, K. W., Mueller, H., Warner, P. B., Shields, D. E., Weeks, H., & Kawamoto, K. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.12.004 · Cited by 6
Buettner, R., Klenk, F., & Ebert, M. (2020) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac48688.2020.00-15 · Cited by 7
“The problem to date is that phenotypes are often recorded in free-text form and therefore such information can easily become obscured and inaccessible [41].”
BackgroundCross-Global Attention Graph Kernel Network Prediction of Drug Prescription
Yao, H.-R., Chang, D., Frieder, O., Huang, W., Liang, I.-C., & Hung, C. (2020) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3388440.3412459 · Cited by 7
“On the other hand, the availability of Electronic Health Records (EHRs), providing historical medical road-maps for patients, enable the development of intelligent predictive systems for drug prescription [3, 33].”
BackgroundYu, 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) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01759-z · Cited by 6
Not all phenotypes are created equal: covariates of success in e-phenotype specification
Hamidi, B., Flume, P. A., Simpson, K. N., & Alekseyenko, A. V. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac157 · Cited by 6
Peterson, H., Vela, K., Barnado, A., & Patrick, A. E. (2023) · Seminars in Arthritis and Rheumatism · doi:10.1016/j.semarthrit.2023.152167 · Cited by 6
An Empirical Analysis of Machine Learning Classifiers for Clinical Decision Making in Asthma
Pooja, M. R., & Pushpalatha, M. (2018) · Communications in computer and information science · doi:10.1007/978-981-10-9059-2_11 · Cited by 6
“A variety of approaches for classifying patients into a particular phenotype exists and a diverse collection of techniques and sources of data are used, and good performance is reported on datasets as per the reviews conducted in [10, 27, 28].”
BackgroundKrantz, M. S., Kerchberger, V. E., & Wei, W. (2022) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2022.04.016 · Cited by 6
Ziemssen, T., Rothenbacher, D., Kühle, J., & Berger, T. (2017) · Der Nervenarzt · doi:10.1007/s00115-017-0387-y · Cited by 6
Chen, Y., Dong, M., Sun, J., Meng, Z., Yang, Y., Muhetaier, A., Li, C., & Qin, J. (2025) · JMIR Medical Informatics · doi:10.2196/70967 · Cited by 5
“Traditionally, medical natural language processing (NLP) techniques have been used to extract structured data elements from medical records, particularly in the field of radiology reports [8,9].”
Adaptable graph neural networks design to support generalizability for clinical event prediction
Tariq, A., Kaur, G., Su, L., Gichoya, J. W., Patel, B. N., & Banerjee, I. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104794 · Cited by 5
Decentralized Learning with Virtual Patients for Medical Diagnosis of Diabetes
Takahashi, Y., Chang, H., Nakai, A., Kagawa, R., Ando, H., Imakura, A., Okada, Y., Tsurushima, H., Suzuki, K., & Sakurai, T. (2021) · SN Computer Science · doi:10.1007/s42979-021-00564-1 · Cited by 5
“Since structured data on diagnoses are limited in terms of accuracy and completeness [14, 37, 53], automated techniques for identifying patients diagnosed with a particular disease based on medical data have increased [11, 21, 22, 41, 52].”
BackgroundTime-Guided High-Order Attention Model of Longitudinal Heterogeneous Healthcare Data
Huang, Y., Yang, X., & Xu, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29908-8_5 · Cited by 5
“Traditional health analysis system often depends on labor intensive efforts, such as expert-defined phenotyping [20,18] and manual feature engineering [24].”
MethodIdentification of Patients with Heart Failure in Large Datasets
KADOSH, B. S., Katz, S. D., & Blecker, S. (2020) · Heart Failure Clinics · doi:10.1016/j.hfc.2020.05.001 · Cited by 5
Brazeal, J. G., Alekseyenko, A. V., Li, H., Fugal, M., Kirchoff, K., Marsh, C. H., Lewin, D., Wu, J. J., Obeid, J. S., & Wallace, K. (2021) · Research Methods in Medicine & Health Sciences · doi:10.1177/26320843211061287 · Cited by 5
Mining Electronic Health Records to Guide and Support Clinical Decision Support Systems
Jonnagaddala, J., Dai, H.-J., RAY, P. K., & Liaw, S. (2017) · IGI Global eBooks · doi:10.4018/978-1-5225-2237-9.ch008 · Cited by 5
Use of instrumental variables in electronic health record-driven models
Salmasi, L., & Capobianco, E. (2016) · Statistical Methods in Medical Research · doi:10.1177/0962280216641154 · Cited by 5
Electronic Health Record Summarization over Heterogeneous and Irregularly Sampled Clinical Data
Pivovarov, R. (2015) · doi:10.7916/d89w0f6v · Cited by 11
Bias of Inaccurate Disease Mentions in Electronic Health Record-based Phenotyping
Kagawa, R., Shinohara, E., Imai, T., Kawazoe, Y., & Ohe, K. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.12.004 · Cited by 5
The effect of number of healthcare visits on study sample selection in electronic health record data
Rasmussen‐Torvik, L. J., Furmanchuk, A., Stoddard, A. J., Osinski, K. I., Meurer, J., Smith, N., Chrischilles, E. A., Black, B. S., & Kho, A. (2020) · International Journal for Population Data Science · doi:10.23889/ijpds.v5i1.1156 · Cited by 5
Self-citationHigh Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Models
Munzir, S. I., Hier, D. B., & Carrithers, M. D. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782119 · Cited by 4
“Improved methods for high throughput phenotyping of electronic health records are needed for precision medicine [8]–[10].”
Zhang, Y., Tayarani, M., Alaref, S. J., Beecy, A., Liu, Y., Sholle, E., RoyChoudhury, A., Axsom, K., Gao, H. O., Pathak, J., et al. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa038 · Cited by 4
Self-citationPre-training phenotyping classifiers
Dligach, D., Afshar, M., & Miller, T. A. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103626 · Cited by 4
“In biomedical informatics, automatic phenotyping [4] refers to identification of cohorts of patients that fit predefined criteria.”
BackgroundPIAT: An Evolutionarily Intelligent System for Deep Phenotyping of Chinese Electronic Health Records
Deng, L., Zhang, X., Yang, T., Liu, M., Chen, L., & Jiang, T. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3177421 · Cited by 5
“The process of structuring phenotype information buried in EHRs is called EHR-based phenotyping [6].”
High-Throughput Phenotyping of Clinical Text Using Large Language Models
Hier, D. B., Munzir, S. I., Stahlfeld, A., Obafemi-Ajayi, T., & Carrithers, M. D. (2024) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi62660.2024.10913712 · Cited by 4
“M ANUAL phenotyping of electronic health records is laborious [1], [2].”
Fu, X., Batista-Navarro, R., Rak, R., & Ananiadou, S. (2014) · Research Explorer (The University of Manchester) · Cited by 4
Sarkar, I. N., Chen, E. S., Rosenau, P. T., Storer, M., Anderson, B., & Horbar, J. D. (2014) · PubMed Central · Cited by 4
Lee, W., Lee, Y., Kim, H., & Moon, I. (2016) · International Joint Conference on Artificial Intelligence · Cited by 4
Estimating Disease Onset Time by Modeling Lab Result Trajectories via Bayes Networks
Oh, W., Yadav, P., Kumar, V., Caraballo, P. J., Castro, M. R., Steinbach, M. S., & Simon, G. (2017) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2017.41 · Cited by 4
“The recent adoption of the electronic health record (EHR) [1], [2] provides us with an opportunity to use it for advanced analytics and thus improving patient health outcomes [3]–[6].”
Overactive bladder in an integrated delivery system: a longitudinal cohort study
Linder, J. A., Weissman, J. S., Nieva, H. R., Lipsitz, S. R., Haring, R. S., Deangelis, J., Kristy, R. M., & Loughlin, K. R. (2020) · BMC Health Services Research · doi:10.1186/s12913-020-05315-1 · Cited by 5
Huang, Y., Yuan, W., Kohane, I. S., & Beaulieu‐Jones, B. K. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab045 · Cited by 4
Identifying surrogate markers in real‐world comparative effectiveness research
Han, L., Wang, X., & Cai, T. (2022) · Statistics in Medicine · doi:10.1002/sim.9569 · Cited by 10
Post-prediction inferencePreprint
Wang, S., McCormick, T. H., & Leek, J. T. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.21.914002 · Cited by 4
“to predict outcomes based on observable covariates [1, 2, 3, 4].”
BackgroundMethodInfluentialSharing and Reusing Computable Phenotype DefinitionsPreprint
Visweswaran, S., Zhang, L. Y., Bui, K., Sadhu, E. M., Samayamuthu, M. J., & Morris, M. M. (2023) · medRxiv · doi:10.1101/2023.09.17.23295681 · Cited by 4
“A key component of the reuse of EHR data is the development, sharing, and application of computable phenotype definitions (computable phenotypes for short) that are computer-executable definitions to identify patients with specific criteria (3, 4).”
BackgroundPheNormGPT: a framework for extraction and normalization of key medical findings
Soysal, E., & Roberts, K. E. (2024) · Database · doi:10.1093/database/baae103 · Cited by 3
“Phenotyping, which is the process of extracting complex trait information from clinical text, is a crucial part of this field that supports a wide range of applications, such as personalized medicine, understanding phenotype–genotype relations, and learning health care systems [1].”
BackgroundMasison, J., Lehmann, H. P., & Wan, J. (2024) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2024.08.025 · Cited by 3
Large language models accurately identify immunosuppression in intensive care unit patients
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Digital systems for improving outcomes in patients with primary immune defects
Rider, N. L. (2020) · Current Opinion in Pediatrics · doi:10.1097/mop.0000000000000963 · Cited by 3
“Advances in the science of used to assemble patient cohorts and provided more high-level identity [40].”
BackgroundAlmowil, Z., Zhou, S., Brophy, S. T., & Croxall, J. (2021) · JMIR Human Factors · doi:10.2196/31021 · Cited by 3
“Phenotyping is the process of extracting phenotypes from clinical data using computer-executable algorithms [11], and phenotypes are “the measurable biological, behavioural and clinical markers of a condition or disease” [12].”
BackgroundMethodComputational phenotyping of obstructive airway diseases: protocol for a systematic review
Bashir, M. B. A., Basna, R., Zhang, G., Bäckman, H., Lindberg, A., Ekerljung, L., Axelsson, M., Hedman, L., Vanfleteren, L. E. G. W., Lundbäck, B., et al. (2022) · Systematic Reviews · doi:10.1186/s13643-022-02078-0 · Cited by 3
“The data-driven approach to phenotyping works through development of high-level computer algorithms that automatically learn from data and try to uncover complex patterns in a systematic and meaningful way (18).”
BackgroundWen, A., He, H., Fu, S., Liu, S., Miller, K., Wang, L., Roberts, K. E., Bedrick, S. D., Hersh, W., & Liu, H. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00878-9 · Cited by 3
“Traditionally conducted manually, there has been great interest in accelerating phenotyping via in-silico means 3,4 .”
BackgroundMiller, M. R., Jorm, L. R., Partyka, C., Burns, B., Habig, K., Oh, C., Immens, S., Ballard, N., & Gallego, B. (2024) · Injury · doi:10.1016/j.injury.2024.111570 · Cited by 4
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
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