Using Electronic Health Records To Generate Phenotypes For Research

Risks and Benefits of Estrogen Plus Progestin in Healthy Postmenopausal Women: Principal Results From the Women's Health Initiative Randomized Controlled Trial

Writing Group for the Women's Health Initiative Investigators (2002) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.288.3.321 · Cited by 15,889 · see citing works

Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. Adults

Calle, E. E., Rodriguez, C., Walker-Thurmond, K., & Thun, M. J. (2003) · New England Journal of Medicine · doi:10.1056/nejmoa021423 · Cited by 7,696 · see citing works

CDC growth charts: United States.

Kuczmarski, R. J., Ogden, C. L., Grummer‐Strawn, L. M., Flegal, K. M., Guo, S. S., Wei, R. X., Mei, Z., Curtin, L. R., Roche, A. F., & Johnson, C. L. (2000) · PubMed · Cited by 5,298 · see citing works

Design of the Women’s Health Initiative Clinical Trial and Observational Study

The Women’s Health Initiative Study Group (1998) · Controlled Clinical Trials · doi:10.1016/s0197-2456(97)00078-0 · Cited by 2,580 · see citing works

Epidemiological approaches to heart disease: the Framingham study

Dawber, T. R., Meadors, G. F., & Moore, F. E. (1951) · American Journal of Public Health and the Nations Health · doi:10.2105/ajph.41.3.279 · Cited by 2,148 · see citing works

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 · see citing works

<i>SLCO1B1</i> Variants and Statin-Induced Myopathy — A Genomewide Study

(2008) · New England Journal of Medicine · doi:10.1056/nejmoa0801936 · Cited by 2,043 · see citing works

Natural language processing: an introduction

Nadkarni, P. M., Lucila Ohno-machado, A., & Chapman, W. W. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000464 · Cited by 1,984 · see citing works

Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications

Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., & Chute, C. G. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.001560 · Cited by 1,967 · see citing works

Factors of Risk in the Development of Coronary Heart Disease—Six-Year Follow-up Experience: The Framingham Study

KANNEL, W. B., DAWBER, T. R., KAGAN, A., REVOTSKIE, N., & STOKES, J. (1961) · Annals of Internal Medicine · doi:10.7326/0003-4819-55-1-33 · Cited by 1,837 · see citing works

Pharmacogenomics Knowledge for Personalized Medicine

Whirl‐Carrillo, M., McDonagh, E. M., Hebert, J. M., Gong, L., Sangkuhl, K., Thorn, C. F., Altman, R. B., & Klein, T. E. (2012) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2012.96 · Cited by 1,791 · see citing works

Advances in natural language processing

Hirschberg, J., & Manning, C. D. (2015) · Science · doi:10.1126/science.aaa8685 · Cited by 1,712 · see citing works

Mining electronic health records: towards better research applications and clinical care

Jensen, P. B., Jensen, L. J., & Brunak, S. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3208 · Cited by 1,680 · see citing works

The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective

Mahmood, S. S., Levy, D., Vasan, R. S., & Wang, T. J. (2014) · The Lancet · doi:10.1016/s0140-6736(13)61752-3 · Cited by 1,622 · see citing works

An overview of MetaMap: historical perspective and recent advances

Aronson, A. R., & Lang, F.-M. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.002733 · Cited by 1,519 · see citing works

The Benefits Of Health Information Technology: A Review Of The Recent Literature Shows Predominantly Positive Results

Buntin, M. B., Burke, M. F., Hoaglin, M. C., & Blumenthal, D. (2011) · Health Affairs · doi:10.1377/hlthaff.2011.0178 · Cited by 1,311 · see citing works

PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene–disease associations

Denny, J. C., Ritchie, M. D., Basford, M. A., Pulley, J. M., Bastarache, L., Brown-Gentry, K., Wang, D., Masys, D. R., Roden, D. M., & Crawford, D. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq126 · Cited by 1,219 · see citing works

Measuring Diagnoses: ICD Code Accuracy

O’Malley, K. J., Cook, K. F., Price, M. D., Wildes, K. R., Hurdle, J. F., & Ashton, C. M. (2005) · Health Services Research · doi:10.1111/j.1475-6773.2005.00444.x · Cited by 1,121 · see citing works

Pharmacogenomics in the clinic

Relling, M. V., & Evans, W. E. (2015) · Nature · doi:10.1038/nature15817 · Cited by 955 · see citing works

II. Coronary Heart Disease in the Framingham Study

Dawber, T. R., Moore, F. E., & Mann, G. V. (1957) · American Journal of Public Health and the Nations Health · doi:10.2105/ajph.47.4_pt_2.4 · Cited by 718 · see citing works

The eMERGE Network: A consortium of biorepositories linked to electronic medical records data for conducting genomic studies

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 692 · see citing works

Using electronic health records for population health research: a review of methods and applications

Casey, J. A., Schwartz, B. S., Stewart, W. F., & Adler, N. E. (2016) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-032315-021353 · Cited by 590 · see citing works

LOINC, a Universal Standard for Identifying Laboratory Observations: A 5-Year Update

McDonald, C. J., Huff, S. M., Suico, J. G., Hill, G., Leavelle, D., Aller, R., Forrey, A., Mercer, K., DeMoor, G., Hook, J., et al. (2003) · Clinical Chemistry · doi:10.1373/49.4.624 · Cited by 579 · see citing works

Caveats for the Use of Operational Electronic Health Record Data in Comparative Effectiveness Research

Hersh, W. R., Weiner, M. G., Embi, P. J., Logan, J. R., Payne, P. R. O., Bernstam, E. V., Lehmann, H. P., Hripcsak, G., Hartzog, T. H., Cimino, J. J., et al. (2013) · Medical Care · doi:10.1097/mlr.0b013e31829b1dbd · Cited by 567 · see citing works

Identification of type 2 diabetes subgroups through topological analysis of patient similarity

Li, L., Cheng, W., Glicksberg, B. S., Gottesman, O., Tamler, R., Chen, R., Böttinger, E. P., & Dudley, J. T. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa9364 · Cited by 548 · see citing works

Discontinuation of Statins in Routine Care Settings: A Cohort Study

Zhang, H., Plutzky, J., Skentzos, S., Morrison, F., Mar, P., Shubina, M., & Turchin, A. (2013) · Annals of Internal Medicine · doi:10.7326/0003-4819-158-7-201304020-00004 · Cited by 548 · see citing works

A review of approaches to identifying patient phenotype cohorts using electronic health records

Shivade, C., Raghavan, P., Fosler‐Lussier, E., Embí, P. J., Elhadad, N., Johnson, S. B., & Lai, A. M. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001935 · Cited by 533 · see citing works

Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study

Dewey, F. E., Murray, M. F., Overton, J. D., Habegger, L., Leader, J. B., Fetterolf, S. N., O’Dushlaine, C. T., Hout, C. V. V., Staples, J. C., Gonzaga-Jauregui, C. G., et al. (2016) · Science · doi:10.1126/science.aaf6814 · Cited by 533 · see citing works

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 · see citing works

Normalized names for clinical drugs: RxNorm at 6 years

Nelson, S. J., Zeng, K., Kilbourne, J., Powell, T., & Moore, R. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000116 · Cited by 460 · see citing works

Supporting information retrieval from electronic health records: A report of University of Michigan’s nine-year experience in developing and using the Electronic Medical Record Search Engine (EMERSE)

Hanauer, D. A., Mei, Q., Law, J., Khanna, R., & Zheng, K. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.05.003 · Cited by 437 · see citing works

Genetic identification of familial hypercholesterolemia within a single U.S. health care system

Abul-Husn, N. S., Manickam, K., Jones, L. K., Wright, E. A., Hartzel, D. N., Gonzaga-Jauregui, C., O’Dushlaine, C., Leader, J. B., Lester Kirchner, H., Lindbuchler, D. M., et al. (2016) · Science · doi:10.1126/science.aaf7000 · Cited by 425 · see citing works

HITECH Act Drove Large Gains In Hospital Electronic Health Record Adoption

Adler‐Milstein, J., & Jha, A. K. (2017) · Health Affairs · doi:10.1377/hlthaff.2016.1651 · Cited by 397 · see citing works

PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability

Kirby, J., Speltz, P., Rasmussen, L. V., Basford, M., Gottesman, O., Peissig, P. L., Pacheco, J. A., Tromp, G. C., Pathak, J., Carrell, D., et al. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv202 · Cited by 369 · see citing works

Insights From Advanced Analytics At The Veterans Health Administration

Fihn, S. D., Francis, J., Clancy, C. M., Nielson, C., Nelson, K. M., Rumsfeld, J. S., Cullen, T., Bates, J., & Graham, G. L. (2014) · Health Affairs · doi:10.1377/hlthaff.2014.0054 · Cited by 368 · see citing works

Medicare and Medicaid programs; electronic health record incentive program--stage 2. Final rule.

(2010) · PubMed · Cited by 360 · see citing works

Uses of Electronic Health Records for Public Health Surveillance to Advance Public Health

Birkhead, G. S., Klompas, M., & Shah, N. R. (2015) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-031914-122747 · Cited by 345 · see citing works

Using electronic health records to drive discovery in disease genomics

Kohane, I. S. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2999 · Cited by 325 · see citing works

Robust Replication of Genotype-Phenotype Associations across Multiple Diseases in an Electronic Medical Record

Ritchie, M. D., Denny, J. C., Crawford, D. C., Ramirez, A. H., Weiner, J., Pulley, J. M., Basford, M., Brown-Gentry, K. D., Balser, J. R., Masys, D. R., et al. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.03.003 · Cited by 320 · see citing works

Patients in context--EHR capture of social and behavioral determinants of health

Adler, N. E., & Stead, W. W. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1413945 · Cited by 317 · see citing works

Clinical Benefits of Electronic Health Record Use: National Findings

King, J., Patel, V., Jamoom, E. W., & Furukawa, M. F. (2013) · Health Services Research · doi:10.1111/1475-6773.12135 · Cited by 277 · see citing works

Logical observation identifier names and codes (LOINC) database: a public use set of codes and names for electronic reporting of clinical laboratory test results

Forrey, A. W., McDonald, C. J., DeMoor, G., Huff, S. M., Leavelle, D., Leland, D., Fiers, T., Charles, L., Griffin, B., Stalling, F., et al. (1996) · Clinical Chemistry · doi:10.1093/clinchem/42.1.81 · Cited by 277 · see citing works

Unravelling the human genome–phenome relationship using phenome-wide association studies

Bush, W. S., Oetjens, M. T., & Crawford, D. C. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.36 · Cited by 267 · see citing works

VistA—U.S. Department of Veterans Affairs national-scale HIS

Brown, S. (2002) · International Journal of Medical Informatics · doi:10.1016/s1386-5056(02)00131-4 · Cited by 238 · see citing works

Patient portals and broadband internet inequality

Perzynski, A. T., Roach, M. J., Shick, S., Callahan, B., Gunzler, D. D., Cebul, R. D., Kaelber, D. C., Huml, A. M., Thornton, J. D., & Einstadter, D. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx020 · Cited by 222 · see citing works

Electronic health records and national patient-safety goals

Sittig, D. F., & Singh, H. (2012) · New England Journal of Medicine · doi:10.1056/nejmsb1205420 · Cited by 204 · see citing works

The Digital Divide and Patient Portals

Graetz, I., Gordon, N. P., Fung, V., Hamity, C., & Reed, M. (2016) · Medical Care · doi:10.1097/mlr.0000000000000560 · Cited by 204 · see citing works

Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams

Shameer, K., Badgeley, M. A., Miotto, R., Glicksberg, B. S., Morgan, J. W., & Dudley, J. T. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbv118 · Cited by 198 · see citing works

Information extraction from multi-institutional radiology reports

Hassanpour, S., & Langlotz, C. P. (2016) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2015.09.007 · Cited by 182 · see citing works

Hormone Therapy for the Primary Prevention of Chronic Conditions in Postmenopausal Women

US Preventive Services Task Force, Grossman, D. C., Curry, S. J., Owens, D. K., Barry, M. J., Davidson, K. W., Doubeni, C. A., Epling, J. W., Kemper, A. R., Krist, A. H., et al. (2017) · JAMA · doi:10.1001/jama.2017.18261 · Cited by 181 · see citing works

Semi-supervised learning of the electronic health record for phenotype stratification

Beaulieu‐Jones, B. K., & Greene, C. S. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.10.007 · Cited by 175 · see citing works

Characterizing and Managing Missing Structured Data in Electronic Health Records: Data Analysis

Beaulieu‐Jones, B. K., Lavage, D. R., Snyder, J. W., Moore, J. H., Pendergrass, S. A., & Bauer, C. R. (2018) · JMIR Medical Informatics · doi:10.2196/medinform.8960 · Cited by 169 · see citing works

The Origins, Development, and Passage of Medicare's Revolutionary Prospective Payment System

Mayes, R. (2006) · Journal of the History of Medicine and Allied Sciences · doi:10.1093/jhmas/jrj038 · Cited by 156 · see citing works

ICD-9 tobacco use codes are effective identifiers of smoking status

Wiley, L. K., Shah, A., Xu, H., & Bush, W. S. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001557 · Cited by 152 · see citing works

Physician Satisfaction with Two Order Entry Systems

Murff, H. J., & Kannry, J. L. (2001) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2001.0080499 · Cited by 152 · see citing works

Patient privacy in the era of big data

Kayaalp, M. (2018) · Balkan Medical Journal · doi:10.4274/balkanmedj.2017.0966 · Cited by 142 · see citing works

Impact of the HITECH Act on physicians’ adoption of electronic health records

Mennemeyer, S. T., Menachemi, N., Rahurkar, S., & Ford, E. W. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv103 · Cited by 140 · see citing works

Implications of an emerging EHR monoculture for hospitals and healthcare systems

Koppel, R., & Lehmann, C. U. (2015) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2014-003023 · Cited by 140 · see citing works

Disease outbreak detection system using syndromic data in the greater Washington DC area1 1Information for the full text of this article is available via AJPM Online at www.ajpm-online.net.

Lewis, M. D., Pavlin, J. A., Mansfield, J., O’Brien, S., Boomsma, L. G., Elbert, Y., & Kelley, P. W. (2002) · American Journal of Preventive Medicine · doi:10.1016/s0749-3797(02)00490-7 · Cited by 138 · see citing works

Natural Language Processing and Its Implications for the Future of Medication Safety: A Narrative Review of Recent Advances and Challenges

Wong, A., Plasek, J. M., Montecalvo, S. P., & Zhou, L. (2018) · Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy · doi:10.1002/phar.2151 · Cited by 127 · see citing works

History of the statistical classification of diseases and causes of death

Moriyama, I. M., Loy, R. M., Robb‐Smith, A. H. T., Rosenberg, H. M., & Hoyert, D. L. (2011) · Cited by 116 · see citing works

Evaluating Foundational Data Quality in the National Patient-Centered Clinical Research Network (PCORnet®)

Qualls, L. G., Phillips, T. A., Hammill, B. G., Topping, J., Louzao, D. M., Brown, J. S., Curtis, L. H., & Marsolo, K. (2018) · eGEMs (Generating Evidence &amp; Methods to improve patient outcomes) · doi:10.5334/egems.199 · Cited by 116 · see citing works

A Disadvantaged Advantage in Walkability: Findings From Socioeconomic and Geographical Analysis of National Built Environment Data in the United States

King, K. E., & Clarke, P. J. (2014) · American Journal of Epidemiology · doi:10.1093/aje/kwu310 · Cited by 111 · see citing works

Natural Language Processing

Joshi, A. K. (1991) · Science · doi:10.1126/science.253.5025.1242 · Cited by 101 · see citing works

Achieving Meaningful Use of Health Information Technology

Seidman, J. (2012) · Archives of Internal Medicine · doi:10.1001/archinternmed.2012.872 · Cited by 99 · see citing works

A Large Genome-Wide Association Study of Age-Related Hearing Impairment Using Electronic Health Records

Hoffmann, T. J., Keats, B. J. B., Yoshikawa, N., Schaefer, C. A., RISCH, N. J., & Lustig, L. R. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006371 · Cited by 99 · see citing works

Dietary Intake and Physical Activity Assessment: Current Tools, Techniques, and Technologies for Use in Adult Populations

McClung, H. L., Ptomey, L. T., Shook, R. P., Aggarwal, A., Gorczyca, A. M., Sazonov, E. E., Becofsky, K. M., Weiss, R., & Das, S. K. (2018) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2018.06.011 · Cited by 99 · see citing works

Biomedical informatics and translational medicine

Sarkar, I. N. (2010) · Journal of Translational Medicine · doi:10.1186/1479-5876-8-22 · Cited by 98 · see citing works

ICD-9 to ICD-10: evolution, revolution, and current debates in the United States.

Topaz, M., Shafran-Topaz, L., & Bowles, K. H. (2013) · PubMed · Cited by 91 · see citing works

Realizing the full potential of electronic health records: the role of natural language processing

Ohno‐Machado, L. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000501 · Cited by 89 · see citing works

Toward a Learning Health-care System – Knowledge Delivery at the Point of Care Empowered by Big Data and NLP

Kaggal, V. C., Elayavilli, R. K., Mehrabi, S., Pankratz, J. J., Sohn, S., Wang, Y., Li, D., Rastegar-Mojarad, M., Murphy, S. P., Ross, J., et al. (2016) · Biomedical Informatics Insights · doi:10.4137/bii.s37977 · Cited by 75 · see citing works

eMERGEing progress in genomics—the first seven years

Crawford, D. C., Crosslin, D. R., Tromp, G. C., Kullo, I. J., Kuivaniemi, H., Hayes, M. G., Denny, J. C., Bush, W. S., Haines, J. L., Roden, D. M., et al. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00184 · Cited by 74 · see citing works

Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network.

Banda, J. M., Halpern, Y., Sontag, D. A., & Shah, N. H. (2017) · PubMed Central · Cited by 67 · see citing works

Multi-Institutional Sharing of Electronic Health Record Data to Assess Childhood Obesity

Bailey, L. C., Milov, D. E., Kelleher, K. J., Kahn, M. G., Beccaro, M. D., Yu, F., Richards, T. M., & Forrest, C. B. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066192 · Cited by 61 · see citing works

Relational machine learning for electronic health record-driven phenotyping

Peissig, P. L., Costa, V. S., Caldwell, M. D., Rottscheit, C., Berg, R. L., Mendonça, E. A., & Page, D. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.07.007 · Cited by 61 · see citing works

Characteristics of state prescription drug monitoring programs: a state‐by‐state survey

Manasco, A. T., Griggs, C. A., Leeds, R., Langlois, B. K., Breaud, A. H., Mitchell, P. M., & Weiner, S. G. (2016) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4003 · Cited by 57 · see citing works

The discriminatory cost of ICD-10-CM transition between clinical specialties: metrics, case study, and mitigating tools

Boyd, A. D., Li, J. ‘., Burton, M. D., Jonen, M., Gardeux, V., Achour, I., Luo, R. Q., Zenku, I., Bahroos, N., Brown, S. B., et al. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001358 · Cited by 56 · see citing works

Using the PhenX Toolkit to Add Standard Measures to a Study

Hendershot, T. P., Pan, H. H., Haines, J. L., Harlan, W. R., Marazita, M. L., McCarty, C. A., Ramos, E. M., & Hamilton, C. M. (2015) · Current Protocols in Human Genetics · doi:10.1002/0471142905.hg0121s86 · Cited by 54 · see citing works

Use of Electronic Health Records in Disaster Response: The Experience of Department of Veterans Affairs After Hurricane Katrina

Brown, S. H., Fischetti, L. F., Graham, G., Bates, J., Lancaster, A. E., McDaniel, D. B., Gillon, J., Darbe, M., & Kolodner, R. M. (2007) · American Journal of Public Health · doi:10.2105/ajph.2006.104943 · Cited by 53 · see citing works

PDF text classification to leverage information extraction from publication reports

Bui, D. D. A., Del Fiol, G., & Jonnalagadda, S. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.03.026 · Cited by 52 · see citing works

Enhancing Practice Efficiency and Patient Care by Sharing Electronic Health Records.

Keller, M. E., Kelling, S. E., Cornelius, D., Oni, H. A., & Bright, D. R. (2015) · Europe PMC (PubMed Central) · Cited by 52 · see citing works

A suite of natural language processing tools developed for the I2B2 project.

(2006) · PubMed Central · Cited by 51 · see citing works

Development of an optical character recognition pipeline for handwritten form fields from an electronic health record

Rasmussen, L. V., Peissig, P. L., McCarty, C. A., & Starren, J. B. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000182 · Cited by 47 · see citing works

Hedging their mets: the use of uncertainty terms in clinical documents and its potential implications when sharing the documents with patients.

Hanauer, D. A., Liu, Y., Mei, Q., Manion, F. J., Balis, U. J., & Zheng, K. (2012) · PubMed Central · Cited by 45 · see citing works

Comparing Electronic Health Record Portals to Obtain Patient-Entered Family Health History in Primary Care

Murray, M., Giovanni, M. A., Klinger, E. V., George, E., Marinacci, L. X., Getty, G., Brawarsky, P., Rocha, B., Orav, E., Bates, D., et al. (2013) · Journal of general internal medicine · doi:10.1007/s11606-013-2442-0 · Cited by 42 · see citing works

Implementation of a “real-world” learning health care system: Washington state's Comparative Effectiveness Research Translation Network (CERTAIN)

Flum, D. R., Alfonso‐Cristancho, R., Devine, E. B., Devlin, A. B., Farrokhi, E. T., Tarczy-Hornoch, P. Z., Kessler, L. G., Lavallee, D. C., Patrick, D. L., Gore, J. L., et al. (2014) · Surgery · doi:10.1016/j.surg.2014.01.004 · Cited by 42 · see citing works

There Are Important Reasons For Delaying Implementation Of The New ICD-10 Coding System

Chute, C. G., Huff, S. M., Ferguson, J. A., Walker, J. M., & Halamka, J. D. (2012) · Health Affairs · doi:10.1377/hlthaff.2011.1258 · Cited by 42 · see citing works

Genome-wide study of resistant hypertension identified from electronic health records

Dumitrescu, L. C., Ritchie, M. D., Denny, J. C., Rouby, N. M. E., McDonough, C. W., Bradford, Y., Ramirez, A. H., Bielinski, S. J., Basford, M., Chai, H. S., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171745 · Cited by 41 · see citing works

Enhancing Electronic Health Record Data with Geospatial Information.

Xie, S., Greenblatt, R. E., Levy, M. Z., & Himes, B. E. (2017) · PubMed Central · Cited by 41 · see citing works

Mining the Electronic Health Record for Disease Knowledge

Chen, E. S., & Sarkar, I. N. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-0709-0_15 · Cited by 37 · see citing works

The Effect of the Health Insurance Portability and Accountability Act of 1996 (HIPAA) on Health Care Fraud in Montana

Eddy, A. (2000) · The Mathematics Enthusiast · Cited by 36 · see citing works

New Pharmacogenomics Research Network: An Open Community Catalyzing Research and Translation in Precision Medicine

Relling, M. V., Krauss, R. M., Roden, D. M., Klein, T. E., Fowler, D. M., Terada, N., Lin, L., Riel-Mehan, M. M., Do, T., Kubo, M., et al. (2017) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.755 · Cited by 35 · see citing works

What makes an EHR "open" or interoperable?

Sittig, D. F., & Wright, A. (2015) · J. Am. Medical Informatics Assoc. · doi:10.1093/jamia/ocv060 · Cited by 29 · see citing works

RxTerms - a drug interface terminology derived from RxNorm.

Fung, K. W., McDonald, C. J., & Bray, B. E. (2008) · PubMed Central · Cited by 28 · see citing works

DEVELOPMENT AND PERFORMANCE OF TEXT-MINING ALGORITHMS TO EXTRACT SOCIOECONOMIC STATUS FROM DE-IDENTIFIED ELECTRONIC HEALTH RECORDS

Hollister, B. M., RESTREPO, N. A., Farber‐Eger, E. H., Crawford, D. C., Aldrich, M. C., & Non, A. L. (2016) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813207813_0023 · Cited by 26 · see citing works

A content analysis of electronic health record (EHR) functionality to support tobacco treatment

Schindler-Ruwisch, J. M., Abroms, L. C., Bernstein, S. L., & Heminger, C. L. (2016) · Translational Behavioral Medicine · doi:10.1007/s13142-016-0446-0 · Cited by 25 · see citing works

Electronic Health Record Phenotypes for Precision Medicine: Perspectives and Caveats From Treatment of Breast Cancer at a Single Institution

Breitenstein, M. K., Liu, H., Maxwell, K. N., Pathak, J., & Zhang, R. (2017) · Clinical and Translational Science · doi:10.1111/cts.12514 · Cited by 22 · see citing works

Reducing Clinical Noise for Body Mass Index Measures Due to Unit and Transcription Errors in the Electronic Health Record.

Goodloe, R., Farber‐Eger, E. H., Boston, J., Crawford, D. C., & Bush, W. S. (2017) · PubMed Central · Cited by 22 · see citing works

Association of chromosome 9p21 SNPs with cardiovascular phenotypes in morbid obesity using electronic health record data

Wood, G. C., Still, C. D., Chu, X., Susek, M., Erdman, R., Hartman, C., Yeager, S. F., Blosky, M. A., Krum, W., Carey, D. J., et al. (2008) · Genomic Medicine · doi:10.1007/s11568-008-9023-z · Cited by 19 · see citing works

The Meaningful Use of Electronic Health Records and Health Care Quality

Kern, L. M., Edwards, A. M., Kaushal, R., & with the HITEC Investigators (2014) · American Journal of Medical Quality · doi:10.1177/1062860614546547 · Cited by 17 · see citing works

The Impact of Electronic Medical Records on Hospital-Acquired Adverse Safety Events: Differential Effects Between Single-Source and Multiple-Source Systems

Bae, J., Rask, K. J., & Becker, E. R. (2017) · American Journal of Medical Quality · doi:10.1177/1062860617702453 · Cited by 16 · see citing works

Effects of Shared Electronic Health Record Systems on Drug-Drug Interaction and Duplication Warning Detection

Rinner, C., Grossmann, W., Sauter, S. K., Wolzt, M., & Gall, W. (2015) · BioMed Research International · doi:10.1155/2015/380497 · Cited by 15 · see citing works

Extracting Primary Open-Angle Glaucoma from Electronic Medical Records for Genetic Association Studies

RESTREPO, N. A., Farber‐Eger, E. H., Goodloe, R., Haines, J. L., & Crawford, D. C. (2015) · PLoS ONE · doi:10.1371/journal.pone.0127817 · Cited by 15 · see citing works

INTEGRATING CLINICAL LABORATORY MEASURES AND ICD-9 CODE DIAGNOSES IN PHENOME-WIDE ASSOCIATION STUDIES

Verma, A., Leader, J. B., Verma, S. S., Frase, A., Wallace, J. R., DUDEK, S. M., Lavage, D. R., Hout, C. V. V., Dewey, F. E., PENN, J., et al. (2015) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814749411_0016 · Cited by 15 · see citing works

Phenotyping Adverse Drug Reactions: Statin-Related Myotoxicity.

Wiley, L. K., Moretz, J. D., Denny, J. C., Peterson, J. F., & Bush, W. S. (2015) · Europe PMC (PubMed Central) · Cited by 14 · see citing works

Contrasting Association Results between Existing PheWAS Phenotype Definition Methods and Five Validated Electronic Phenotypes.

Leader, J. B., Pendergrass, S. A., Verma, A., Carey, D. J., Hartzel, D. N., Ritchie, M. D., & Kirchner, H. L. (2015) · PubMed Central · Cited by 14 · see citing works

Modest Associations Between Electronic Health Record Use and Acute Myocardial Infarction Quality of Care and Outcomes

Enriquez, J. R., de Lemos, J. A., Parikh, S. V., Simon, D. N., Thomas, L. E., Wang, T. Y., Chan, P. S.-F., Spertus, J. A., & Das, S. R. (2015) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.115.001837 · Cited by 13 · see citing works

IDENTIFYING GENETIC ASSOCIATIONS WITH VARIABILITY IN METABOLIC HEALTH AND BLOOD COUNT LABORATORY VALUES: DIVING INTO THE QUANTITATIVE TRAITS BY LEVERAGING LONGITUDINAL DATA FROM AN EHR

Verma, S. S., Lucas, A., Lavage, D. R., Leader, J. B., Metpally, R. P. R., Krishnamurthy, S. B., Dewey, F. E., Borecki, I. B., Lopez, A. E., Overton, J. D., et al. (2016) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813207813_0049 · Cited by 12 · see citing works

Examining the Food Retail Choice Context in Urban Food Deserts, Ohio, 2015

Moore, S. P., Trapl, E., Clark, J. K., Rouse, C. D., Bell, B. A., Sehgal, A. R., To, T., Borawski, E. A., & Freedman, D. A. (2017) · Preventing Chronic Disease · doi:10.5888/pcd14.160408 · Cited by 11 · see citing works

Sri Lankan sanni masks: an ancient classification of disease

Bailey, M. S., & de Silva, H. J. (2006) · BMJ · doi:10.1136/bmj.39055.445417.be · Cited by 10 · see citing works

What's So Meaningful about Meaningful Use?

Galbraith, K. L. (2013) · The Hastings Center Report · doi:10.1002/hast.154 · Cited by 10 · see citing works

iGAS: A framework for using electronic intraoperative medical records for genomic discovery

Levin, M. A., Joseph, T. T., Jeff, J. M., Nadukuru, R., Ellis, S. B., Böttinger, E. P., & Kenny, E. E. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.02.005 · Cited by 9 · see citing works

Menopausal Hormone Therapy for Primary Prevention of Chronic Disease

Lewis, C. E., & Wellons, M. (2017) · JAMA · doi:10.1001/jama.2017.16974 · Cited by 9 · see citing works

THE CHALLENGES IN USING ELECTRONIC HEALTH RECORDS FOR PHARMACOGENOMICS AND PRECISION MEDICINE RESEARCH

Laper, S. M., RESTREPO, N. A., & Crawford, D. C. (2015) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814749411_0034 · Cited by 8 · see citing works

Genetically-guided algorithm development and sample size optimization for age-related macular degeneration cases and controls in electronic health records from the VA Million Veteran Program.

Halladay, C. W., Hadi, T. M., Anger, M. D., Greenberg, P. B., Sullivan, J. M., Konicki, P. E., Peachey, N. S., Igo, R. P., Iyengar, S. K., Wu, W., et al. (2019) · PubMed Central · Cited by 8 · see citing works

OPENING THE DOOR TO THE LARGE SCALE USE OF CLINICAL LAB MEASURES FOR ASSOCIATION TESTING: EXPLORING DIFFERENT METHODS FOR DEFINING PHENOTYPES

Bauer, C. R., Lavage, D. R., SNYDER, J., Leader, J. B., Mahoney, J. M., & Pendergrass, S. A. (2016) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813207813_0034 · Cited by 5 · see citing works

Machine Learning for Structured Clinical Data

Beaulieu‐Jones, B. K. (2017) · Intelligent systems reference library · doi:10.1007/978-3-319-67513-8_3 · Cited by 5 · see citing works

Using Adipose Measures from Health Care Provider-Based Imaging Data for Discovery

Cha, E. D. K., Veturi, Y., Agarwal, C., Patel, A. A., Arbabshirani, M. R., & Pendergrass, S. A. (2018) · Journal of Obesity · doi:10.1155/2018/3253096 · Cited by 5 · see citing works

Extracting Country-of-Origin from Electronic Health Records for Gene- Environment Studies as Part of the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) Study.

Farber‐Eger, E. H., Goodloe, R., Boston, J., Bush, W. S., & Crawford, D. C. (2017) · PubMed Central · Cited by 5 · see citing works

Classification of reported statin intolerance

Preiss, D., & Sattar, N. A. (2014) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000149 · Cited by 3 · see citing works

TESTING POPULATION-SPECIFIC QUANTITATIVE TRAIT ASSOCIATIONS FOR CLINICAL OUTCOME RELEVANCE IN A BIOREPOSITORY LINKED TO ELECTRONIC HEALTH RECORDS: LPA AND MYOCARDIAL INFARCTION IN AFRICAN AMERICANS

Dumitrescu, L. C., Diggins, K. E., Goodloe, R., & Crawford, D. C. (2015) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814749411_0010 · Cited by 3 · see citing works

INTEGRATING COMMUNITY-LEVEL DATA RESOURCES FOR PRECISION MEDICINE RESEARCH

Bush, W. S., Crawford, D. C., Briggs, F. B. S., Freedman, D. A., & Sloan, C. (2017) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813235533_0057 · Cited by 3 · see citing works

Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application

Peissig, P. L., Schwei, K. M., Kadolph, C., Finamore, J., Cancel, E., McCarty, C. A., Okorie, A., Thomas, K. L., Pacheco, J. A., Pathak, J., et al. (2017) · JMIR Medical Informatics · doi:10.2196/medinform.7465 · Cited by 2 · see citing works

A brief history of human disease genetics

Claussnitzer, M., Cho, J. H., Collins, R., Cox, N. J., Dermitzakis, E. T., Hurles, M. E., Kathiresan, S., Kenny, E. E., Lindgren, C. M., MacArthur, D. G., et al. (2020) · Nature · doi:10.1038/s41586-019-1879-7 · Cited by 697 · see citing works

Personalized Medicine and the Power of Electronic Health Records

Abul-Husn, N. S., & Kenny, E. E. (2019) · Cell · doi:10.1016/j.cell.2019.02.039 · Cited by 355 · see citing works

Precision Medicine: Disease Subtyping and Tailored Treatment

Wang, R. C., & Wang, Z. (2023) · Cancers · doi:10.3390/cancers15153837 · Cited by 337 · see citing works

Association of step counts over time with the risk of chronic disease in the All of Us Research Program

Master, H., Annis, J., Huang, S., Beckman, J. A., Ratsimbazafy, F., Marginean, K., Carroll, R., Natarajan, K., Harrell, F. E., Roden, D. M., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-02012-w · Cited by 221 · see citing works

Machine learning approaches for electronic health records phenotyping: a methodical review

Yang, S., Varghese, P., Stephenson, E., Tu, K., & Gronsbell, J. L. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac216 · Cited by 142 · see citing works

The role of blockchain to secure internet of medical things

Ghadi, Y. Y., Mazhar, T., Shahzad, T., Khan, M. A., Abd-Alrazaq, A. A., Ahmed, A., & Hamam, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-68529-x · Cited by 127 · see citing works

Genotype first: Clinical genomics research through a reverse phenotyping approach

Wilczewski, C. M., Obasohan, J., Paschall, J. E., Zhang, S., Singh, S., Maxwell, G. L., Similuk, M. N., Wolfsberg, T. G., Turner, C., Biesecker, L. G., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.12.004 · Cited by 71 · see citing works

Biases in Electronic Health Records Data for Generating Real-World Evidence: An Overview

Al‐Sahab, B., Leviton, A., Loddenkemper, T., Paneth, N., & Zhang, B. (2023) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-023-00153-2 · Cited by 71 · see citing works

Association of Bariatric Surgery With Risk of Fracture in Patients With Severe Obesity

Khalid, S. I., Omotosho, P. A., Spagnoli, A., & Torquati, A. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.7419 · Cited by 56 · see citing works

With big data comes big responsibility: Strategies for utilizing aggregated, standardized, de‐identified electronic health record data for research

Olaker, V. R., Fry, S., Terebuh, P., Davis, P. B., Tisch, D. J., Xu, R., Miller, M. G., Dorney, I., Palchuk, M. B., & Kaelber, D. C. (2024) · Clinical and Translational Science · doi:10.1111/cts.70093 · Cited by 55 · see citing works

Comparing medical history data derived from electronic health records and survey answers in the All of Us Research Program

Sulieman, L. M., Cronin, R. M., Carroll, R. J., Natarajan, K., Marginean, K., Mapes, B. M., Roden, D. M., Harris, P. A., & Ramirez, A. H. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac046 · Cited by 43 · see citing works

Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia

Wu, C., Luedtke, A. R., Sadikova, E., Tsai, H., Liao, S., Liu, C., Gau, S. S., VanderWeele, T. J., & Kessler, R. C. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.21660 · Cited by 42 · see citing works

Using natural language processing to identify opioid use disorder in electronic health record data

Singleton, J. H., Li, C., Akpunonu, P., Abner, E. L., & Kucharska‐Newton, A. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104963 · Cited by 35 · see citing works

Opportunities and Challenges of Cardiovascular Disease Risk Prediction for Primary Prevention Using Machine Learning and Electronic Health Records: A Systematic Review

Liu, T., Krentz, A. J., Huo, Z., & Ćurčin, V. (2025) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm37443 · Cited by 43 · see citing works

Incidence of hepatocellular carcinoma in nonalcoholic fatty liver disease without cirrhosis or advanced liver fibrosis

Behari, J., Gougol, A., Wang, R., Luu, H. N., Paragomi, P., Yu, Y.-C., Molinari, M., Chopra, K., Malik, S. M., Geller, D., et al. (2023) · Hepatology Communications · doi:10.1097/hc9.0000000000000183 · Cited by 35 · see citing works

OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical recordsPreprint

Chen, Q., Dwaraka, V. B., Carreras-Gallo, N., Mendez, K. M., Chen, Y., Begum, S., Kachroo, P., Prince, N., Went, H., Mendez, T. L., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.16.562114 · Cited by 32 · see citing works

Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology

Mathis, M. R., Steffner, K. R., Subramanian, H., Gill, G., Girardi, N. I., Bansal, S., Bartels, K., Khanna, A. K., & Huang, J. (2024) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2024.02.004 · Cited by 29 · see citing works

Clinical risk prediction using language models: benefits and considerations

Acharya, A., Shrestha, S., Chen, A., Conte, J., Avramovic, S., Sikdar, S., Anastasopoulos, A., & Das, S. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae030 · Cited by 27 · see citing works

Optimising the use of electronic medical records for large scale research in psychiatry

Newby, D., Taylor, N., Joyce, D. W., & Winchester, L. M. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02911-1 · Cited by 29 · see citing works

Genetic architecture of routinely acquired blood tests in a British South Asian cohort

Jacobs, B. M., Stow, D., Hodgson, S., Zöllner, J., Samuel, M., Kanoni, S., Bidi, S., Akhtar, S., Anwar, M., Arciero, E., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53091-x · Cited by 26 · see citing works

Maturation and application of phenome-wide association studies

Liu, S., & Crawford, D. C. (2022) · Trends in Genetics · doi:10.1016/j.tig.2021.12.002 · Cited by 23 · see citing works

Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants

Cruz, L. A., Bailey, J. N. C., & Crawford, D. C. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-122220-113250 · Cited by 22 · see citing works

Harnessing the Power of Electronic Health Records and Genomics for Drug Discovery

Krebs, K., & Milani, L. A. (2023) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-051421-111324 · Cited by 19 · see citing works

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Avery, C. L., Howard, A. G., Ballou, A. F., Buchanan, V. L., Collins, J. M., Downie, C. G., Engel, S. M., Graff, M., Highland, H. M., Lee, M. P., et al. (2022) · Environmental Health Perspectives · doi:10.1289/ehp9098 · Cited by 18 · see citing works

Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems

Walters, C. E., Nitin, R., Margulis, K., Boorom, O., Gustavson, D. E., Bush, C. T., Davis, L. K., Below, J. E., Cox, N. J., Camarata, S. M., et al. (2020) · Journal of Speech, Language, and Hearing Research · doi:10.1044/2020_jslhr-19-00397 · Cited by 18 · see citing works

OMICmAge quantifies biological age by integrating multi-omics with electronic medical records

Chen, Q., Dwaraka, V. B., Carreras-Gallo, N., Armstrong, J., Sehgal, R., Argentieri, M. A., Richmond, A., Aparicio, A., Mendez, K. M., Chen, Y., et al. (2026) · Nature Aging · doi:10.1038/s43587-026-01073-7 · Cited by 18 · see citing works

Using electronic health records for clinical pharmacology research: Challenges and considerations

Jafari, E. A., Blackman, M. H., Karnes, J. H., Driest, S. L. V., Crawford, D. C., Choi, L., & McDonough, C. W. (2024) · Clinical and Translational Science · doi:10.1111/cts.13871 · Cited by 17 · see citing works

A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans

Pendergrass, S. A., Buyske, S. G., Jeff, J. M., Frase, A., DUDEK, S. M., Bradford, Y., Ambite, J. L., Avery, C. L., Bůžková, P., Deelman, E., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0226771 · Cited by 17 · see citing works

Derivation and validation of generalized sepsis-induced acute respiratory failure phenotypes among critically ill patients: a retrospective study

Choudhary, T., Upadhyaya, P., Davis, C., Yang, P., Tallowin, S., Lisboa, F., Schobel, S., Coopersmith, C. M., Elster, E. A., Buchman, T. G., et al. (2024) · Critical Care · doi:10.1186/s13054-024-05061-4 · Cited by 16 · see citing works

Development and validation of algorithms to identify patients with chronic kidney disease and related chronic diseases across the Northern Territory, Australia

Chen, W. T., Abeyaratne, A., Gorham, G., George, P. J., Karepalli, V., Tran, D., Brock, C., & Cass, A. (2022) · BMC Nephrology · doi:10.1186/s12882-022-02947-9 · Cited by 16 · see citing works

Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review

Pruinelli, L., Balakrishnan, K., Ma, S., Li, Z., Wall, A. E., Lai, J. C., Schold, J. D., Pruett, T. L., & Simon, G. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02890-3 · Cited by 15 · see citing works

Exploring the Use of Genomic and Routinely Collected Data: Narrative Literature Review and Interview Study

Daniels, H., Jones, K. H., Heys, S., & Ford, D. V. (2021) · Journal of Medical Internet Research · doi:10.2196/15739 · Cited by 15 · see citing works

Polygenic Scores Help Reduce Racial Disparities in Predictive Accuracy of Automated Type 1 Diabetes Classification Algorithms

Deutsch, A. J., Stalbow, L. A., Majarian, T. D., Mercader, J. M., Manning, A. K., Florez, J. C., Loos, R. J. F., & Udler, M. S. (2023) · Diabetes Care · doi:10.2337/dc22-1833 · Cited by 15 · see citing works

Identification of Opioid Use Disorder Using Electronic Health Records: Beyond Diagnostic Codes

Poulsen, M., Nordberg, C., Troiani, V., Berrettini, W., Asdell, P. B., & Schwartz, B. S. (2023) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2023.110950 · Cited by 16 · see citing works

The GA4GH Phenopacket schema: A computable representation of clinical data for precision medicinePreprint

Jacobsen, J. O. B., Baudis, M., Baynam, G. S., Beckmann, J. S., Beltrán, S., Callahan, T. J., Chute, C. G., Courtot, M., Daniš, D., Elemento, O., et al. (2021) · medRxiv · doi:10.1101/2021.11.27.21266944 · Cited by 15 · see citing works

Association Between Fuchs Endothelial Corneal Dystrophy, Diabetes Mellitus, and Multimorbidity

Nealon, C. L., Halladay, C. W., Gorman, B. R., Simpson, P., Roncone, D. P., Canania, R. L., Anthony, S. A., Rogers, L. R. S., Leber, J. N., Dougherty, J. M., et al. (2023) · Cornea · doi:10.1097/ico.0000000000003311 · Cited by 14 · see citing works

Combining structured and unstructured data in EMRs to create clinically-defined EMR-derived cohorts

Tam, C. S., Gullick, J. G., Saavedra, A. F., Vernon, S. T., Figtree, G. A., Chow, C. K., Cretikos, M. A., Morris, R. W., William, M., Morris, J. M., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01441-w · Cited by 14 · see citing works

Evaluating construct validity of computable acute respiratory distress syndrome definitions in adults hospitalized with COVID-19: an electronic health records based approach

Sathe, N. A., Xian, S., Mabrey, F. L., Crosslin, D. R., Mooney, S. D., Morrell, E. D., Lybarger, K., Yetişgen, M., Jarvik, G. P., Bhatraju, P. K., et al. (2023) · BMC Pulmonary Medicine · doi:10.1186/s12890-023-02560-y · Cited by 13 · see citing works

Leveraging electronic health record data for endometriosis research

Penrod, N. M., Okeh, C. C., Edwards, D. R. V., Barnhart, K. T., Senapati, S., & Verma, S. S. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1150687 · Cited by 12 · see citing works

Using the electronic health record for genomics research

Safarova, M. S., & Kullo, I. J. (2020) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000662 · Cited by 14 · see citing works

Machine learning can identify patients at risk of hyperparathyroidism without known calcium and intact parathyroid hormone

Greer, M., Davis, K. P., & Stack, B. C. (2021) · Head & Neck · doi:10.1002/hed.26970 · Cited by 12 · see citing works

A Digital Case‐Finding Algorithm for Diagnosed but Untreated Hepatitis C: A Tool for Increasing Linkage to Treatment and Cure

Wyatt, B. E., Perumalswami, P., Mageras, A., Miller, M., Harty, A., Ma, N., Bowman, C. A., Collado, F., Jeon, J., Paulino, L., et al. (2021) · Hepatology · doi:10.1002/hep.32086 · Cited by 11 · see citing works

The enome-wide ssociation tudy of erum IgE evels emonstrated a hared enetic ackground in llergic iseases

Lu, H., Chou, C.-H., Lin, Y.-J., Uchiyama, S., Terao, C. C., Wang, Y., Yang, J., Liu, T., Wong, H. S.-C., Chen, S. C.-C., et al. (2024) · Clinical Immunology · doi:10.1016/j.clim.2024.109897 · Cited by 10 · see citing works

A novel method for multiple phenotype association studies based on genotype and phenotype network

Cao, X., Zhang, S., & Sha, Q. (2024) · PLoS Genetics · doi:10.1371/journal.pgen.1011245 · Cited by 10 · see citing works

Development and Evaluation of a Rules-based Algorithm for Primary Open-Angle Glaucoma in the VA Million Veteran Program

Nealon, C. L., Halladay, C. W., Kinzy, T. G., Simpson, P., Canania, R. L., Anthony, S. A., Roncone, D. P., Rogers, L. R. S., Leber, J. N., Dougherty, J. M., et al. (2021) · Ophthalmic Epidemiology · doi:10.1080/09286586.2021.1992784 · Cited by 10 · see citing works

A retrospective analysis using comorbidity detecting algorithmic software to determine the incidence of International Classification of Diseases (ICD) code omissions and appropriateness of Diagnosis-Related Group (DRG) code modifiers

Gabel, E., Gal, J. S., Grogan, T., & Hofer, I. S. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02724-8 · Cited by 9 · see citing works

Assessing the capture of sociodemographic information in electronic medical records to inform clinical decision making

Abulibdeh, R., Tu, K., Butt, D. A., Train, A. D., Crampton, N. H., & Sejdić, E. (2025) · PLoS ONE · doi:10.1371/journal.pone.0317599 · Cited by 9 · see citing works

Pharmacogenetics: Opportunities for the All of Us Research Program and Other Large Data Sets to Advance the Field

Empey, P. E., Karnes, J. H., & Johnson, J. A. (2025) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-061724-080718 · Cited by 10 · see citing works

Label efficient phenotyping for Long COVID using electronic health records

Hong, C., Wen, J., Zhang, H. G., Panickan, V. A., Yang, D. Y., Chen, A. W., Xiong, X., Wang, X., Morris, M. M., Morini, S., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01617-y · Cited by 9 · see citing works

Sepsis in silico: definition, development and application of an electronic phenotype for sepsis

Al-Sultani, Z., Inglis, T. J. J., McFadden, B. R., Thomas, E., & Reynolds, M. (2025) · Journal of Medical Microbiology · doi:10.1099/jmm.0.001986 · Cited by 8 · see citing works

Explainable Machine Learning (XML) for Multimedia-Based Healthcare Systems: Opportunities, Challenges, Ethical and Future Prospects

Awotunde, J. B., Imoize, A. L., Adeniyi, A. E., Abiodun, M. K., Ayo, F. E., Kavitha, K. V. N., Ajamu, G. J., & Ogundokun, R. O. (2023) · Springer eBooks · doi:10.1007/978-3-031-38036-5_2 · Cited by 10 · see citing works

Variations in Electronic Health Record-Based Definitions of Diabetic Retinopathy Cohorts

Chen, J., Copado, I. A., Vallejos, C., Kalaw, F. G. P., Soe, P., Cai, C. X., Toy, B. C., Borkar, D. S., Sun, C. Q., Shantha, J. G., et al. (2024) · Ophthalmology Science · doi:10.1016/j.xops.2024.100468 · Cited by 7 · see citing works

Cox regression is robust to inaccurate EHR-extracted event time: an application to EHR-based GWAS

Irlmeier, R. T., Hughey, J., Bastarache, L. A., Denny, J. C., & Chen, Q. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac086 · Cited by 6 · see citing works

Classifying early infant feeding status from clinical notes using natural language processing and machine learning

Lemas, D. J., Du, X., Rouhizadeh, M., Lewis, B., Frank, S., Wright, L., Spirache, A., Gonzalez, L., Cheves, R., Magalhães, M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-58299-x · Cited by 6 · see citing works

Multisource representation learning for pediatric knowledge extraction from electronic health records

Li, M., Li, X., Pan, K., Geva, A., Yang, D. Y., Sweet, S. M., Bonzel, C.-L., Panickan, V. A., Xiong, X., Mandl, K. D., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01320-4 · Cited by 6 · see citing works

Concept Libraries for Automatic Electronic Health Record Based Phenotyping: A Review

Almowil, Z., Zhou, S., & Brophy, S. T. (2021) · International Journal for Population Data Science · doi:10.23889/ijpds.v6i1.1362 · Cited by 6 · see citing works

Developing a risk stratification tool for predicting opioid-related respiratory depression after non-cardiac surgery: a retrospective study

Roy, S., Bruehl, S. P., Feng, X., Shotwell, M. S., de Ven, T. V., Shaw, A. D., & Kertai, M. D. (2022) · BMJ Open · doi:10.1136/bmjopen-2022-064089 · Cited by 6 · see citing works

Developing electronic health record algorithms that accurately identify patients with juvenile idiopathic arthritis

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 · see citing works

Significant EHR Feature-Driven T2D Inference: Predictive Machine Learning and Networks

Preo, N., & Capobianco, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00030 · Cited by 6 · see citing works

A Blockchain-Empowered Federated Learning System and the Promising Use in Drug Discovery

Liang, X., Bandara, E., Zhao, J., & Shetty, S. S. (2022) · Blockchain technologies · doi:10.1007/978-981-19-2976-2_6 · Cited by 6 · see citing works

Performance of a Computational Phenotyping Algorithm for Sarcoidosis Using Diagnostic Codes in Electronic Medical Records: Case Validation Study From 2 Veterans Affairs Medical Centers

Seedahmed, M. I., Mogilnicka, I., Zeng, S., Luo, G., Whooley, M. A., McCulloch, C. E., Koth, L. L., & Arjomandi, M. (2022) · JMIR Formative Research · doi:10.2196/31615 · Cited by 5 · see citing works

Review of Clinical Research Informatics

Solomonides, A. E. (2020) · Yearbook of Medical Informatics · doi:10.1055/s-0040-1701988 · Cited by 4 · see citing works

Multi-PheWAS intersection approach to identify sex differences across comorbidities in 59 140 pediatric patients with autism spectrum disorder

Gutiérrez‐Sacristán, A., Sáez, C., Niz, C. D., Jalali, N., DeSain, T., Kumar, R., Zachariasse, J. M., Fox, K. P., Palmer, N., Kohane, I. S., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab144 · Cited by 4 · see citing works

Phenome-wide association study in 25,639 pregnant Chinese women reveals loci associated with maternal comorbidities and child health

Guo, J., Guo, Q., Zhong, T., Xu, C., Xia, Z., Fang, H., Chen, Q., Zhou, Y., Xie, J., Jin, D., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100632 · Cited by 4 · see citing works

Advances in mass cytometry and its applicability to digital pathology in clinical-translational cancer research

Cereceda, K., Jorquera, R., & Villarroel‐Espíndola, F. (2021) · Advances in Laboratory Medicine / Avances en Medicina de Laboratorio · doi:10.1515/almed-2021-0075 · Cited by 4 · see citing works

Facilitating cancer systems epidemiology research

Barajas, R., Hair, B. Y., Lai, G. Y., Rotunno, M., Shams‐White, M., Gillanders, E. M., & Mechanic, L. E. (2021) · PLoS ONE · doi:10.1371/journal.pone.0255328 · Cited by 4 · see citing works

Evaluating the representativeness and validity of Cosmos as a novel, large-scale, real-world data source for liver transplant research

Strauss, A. T., Terlizzi, K., Orandi, B. J., Stewart, D. E., Massie, A. B., Vong, T., Jain, V. S., Thompson, V. L., McAdams‐DeMarco, M., Iturrate, E., et al. (2025) · Liver Transplantation · doi:10.1097/lvt.0000000000000728 · Cited by 3 · see citing works

Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records

Hou, Y., Rabbani, S. B., Hong, L., Diawara, N., & Samad, M. D. (2025) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri66576.2025.00030 · Cited by 3 · see citing works

Use of machine learning for chronic disease case identification in primary care settings: A scoping review of methods, validation, and clinical relevance

Pham, A. N., Lindeman, C., Cummings, M., Aponte-Hao, S., & Kjelland, K. (2026) · Journal of International Medical Research · doi:10.1177/03000605251412076 · Cited by 3 · see citing works

An evaluation and comparison of traumatic brain injury data sources within the VA Million Veteran Program: A descriptive epidemiological study

Maihofer, A. X., Coffinier, C., Kellogg, M. A., Brewer, J. V., Brophy, M. T., Cho, K., Churby, L., DuVall, S. L., Pyarajan, S., Ringer, R., et al. (2025) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2025.04.038 · Cited by 3 · see citing works

Predicting In-Hospital Cardiac Arrest Using Machine Learning Models: Protocol for a Scoping Review

Attin, M., Shareef, B., Appiah-Agyei, N., Rini, F. M., Goodman, X. Y., Bredesky, L., Chavez, J. A., Mohammed, R., & Batra, K. (2025) · JMIR Research Protocols · doi:10.2196/69716 · Cited by 3 · see citing works

Concept Libraries for Repeatable and Reusable Research: Qualitative Study Exploring the Needs of Users

Almowil, Z., Zhou, S., Brophy, S. T., & Croxall, J. (2021) · JMIR Human Factors · doi:10.2196/31021 · Cited by 3 · see citing works

Identifying prehospital trauma patients from ambulance patient care records; comparing two methods using linked data in New South Wales, Australia

Miller, 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 3 · see citing works

Current AI applications in medical therapies and services

Catania, L. J. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-824477-7.00013-4 · Cited by 3 · see citing works

Predicting Unplanned Hospital Readmissions using Patient Level Data

Mahesh Balan, U., Gandhi, M., & Rammohan, S. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.417 · Cited by 3 · see citing works

Combining structured and unstructured data in eMRs to create clinically-defined eMR-derived cohortsPreprint

Tam, C. S., Gullick, J. G., Saavedra, A. F., Vernon, S. T., Figtree, G. A., Chow, C. K., Cretikos, M. A., Morris, R. W., William, M., Morris, J. M., et al. (2020) · medRxiv · doi:10.1101/2020.07.27.20163279 · Cited by 3 · see citing works

A survey of the use of EHR as real-world evidence for discovering and validating new drug indications.

Talukdar, N., Zhang, X.-D., Paithankar, S., Wang, H., & Chen, B. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104552 · Cited by 1 · see citing works

Optimizing real-time phenotyping in critical care using machine learning on electronic health records

Picheta, P., & Deniziak, S. R. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132084 · Cited by 2 · see citing works

Trends in publication and levels of social determinants of health reporting in Journal of Clinical and Translational Science from 2017 to 2023

Strekalova, Y. A., Wang, X., Sanchez, O., & Midence, S. (2024) · Journal of Clinical and Translational Science · doi:10.1017/cts.2024.508 · Cited by 2 · see citing works

Development and validation of an electronic health record-based algorithm for identifying TBI in the VA: A VA Million Veteran Program study

Merritt, V. C., Chen, A. W., Bonzel, C.-L., Hong, C., Sangar, R., Sweet, S. M., Sorg, S. F., Coffinier, C., & Program, V. M. V. (2024) · Brain Injury · doi:10.1080/02699052.2024.2373920 · Cited by 2 · see citing works

Phenome-wide association study identifies multiple traits associated with a polygenic risk score for colorectal cancer

Rosenthal, E. A., Wei, W., Luo, Y., Namjou‐Khales, B., Schaid, D. J., Esplin, E. D., Lape, M., Kottyan, L. C., Pacheco, J. A., Weng, C., et al. (2025) · Human Genomics · doi:10.1186/s40246-025-00791-0 · Cited by 2 · see citing works

Are Electronic Health Records Sufficiently Accurate to Phenotype Rheumatology Patients With Chronic Pain?

Clarke, H. A., & Fitzcharles, M. (2024) · The Journal of Rheumatology · doi:10.3899/jrheum.2023-1227 · Cited by 2 · see citing works

The Impact of Big Data Dimensions on the Performance of Jordan's Medical Sector: A Case Study of Al-Bashir Hospital

(2024) · Journal of System and Management Sciences · doi:10.33168/jsms.2024.0626 · Cited by 1 · see citing works

A Dynamic Time Warping Extension to Consensus Weight‐Based Cachexia Criteria Improves Prediction of Cancer Patient Outcomes

Forrest, N. J., Tran, S., Nandoliya, K. R., Houskamp, E. J., Gruchala, T., Guggilla, V., Sun, Z., Lukas, R. V., Wainwright, D. A., Furmanchuk, A., et al. (2025) · JCSM Communications · doi:10.1002/rco2.107 · Cited by 1 · see citing works

Machine-Learned Codes from EHR Data Predict Hard Outcomes Better than Human-Assigned ICD Codes

Yin, Y., Shao, Y., Ma, P., Zeng‐Treitler, Q., & Nelson, S. J. (2025) · Machine Learning and Knowledge Extraction · doi:10.3390/make7020036 · Cited by 1 · see citing works

Response to letter Re: Artificial intelligence to predict cancer risk, are we there yet? A comprehensive review across cancer types

Felici, A., Peduzzi, G., Pellungrini, R., & Campa, D. (2025) · European Journal of Cancer · doi:10.1016/j.ejca.2025.115716 · Cited by 1 · see citing works

A scoping review of models to identify transgender patients in electronic health records

Becker, R. A., Kolli, J., & Walsh, C. G. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf185 · Cited by 1 · see citing works

Comparing Alzheimer disease phenotype extraction using rule-based natural language processing, GPT-4, Phi-4, LLaMA, and DeepSeek

Oh, I. Y., Powell, W. J. B., Schindler, S. E., Ghoshal, N., Lai, A. M., Payne, P., & Gupta, A. (2025) · npj Dementia · doi:10.1038/s44400-025-00043-x · Cited by 1 · see citing works

Multi-ancestry genome-wide association study in all of Us for primary open-angle glaucoma

Tavakoli, K., Huang, B. B., Mirmira, T., Ma, N., Weinreb, R. N., & Baxter, S. L. (2026) · Scientific Reports · doi:10.1038/s41598-026-43993-9 · Cited by 1 · see citing works

Performance Characteristics of a Rule-Based Electronic Health Record Algorithm to Identify Patients with Gross and Microscopic Hematuria

Kashkoush, J. I., Gupta, M., Meissner, M. A., Nielsen, M. E., Kirchner, H. L., & Garg, T. K. (2023) · Methods of Information in Medicine · doi:10.1055/a-2165-5552 · Cited by 1 · see citing works

Frequency of ClinVar Pathogenic Variants in Chronic Kidney Disease Patients Surveyed for Return of Research Results at a Cleveland Public Hospital

Crawford, D. C., Lin, J. P., Bailey, J. N. C., Kinzy, T. G., Sedor, J. R., O’Toole, J. F., & Bush, W. S. (2019) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811215636_0051 · Cited by 1 · see citing works

Special topics in electronic health data

McGrath, L. J., & Wong, J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-817663-4.00011-8 · Cited by 1 · see citing works

Digital Data in Lupus: Metrics and Future Directions

Simard, J. F., Chaichian, Y., & Falasinnu, T. (2021) · Springer eBooks · doi:10.1007/978-3-030-73303-2_10 · Cited by 1 · see citing works

Interpretable Phenotyping for Electronic Health Records

Allen, C. M. B., Hu, J., Kumar, V., Ahmad, M. A., & Teredesai, A. M. (2021) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi52183.2021.00034 · Cited by 1 · see citing works

Medication based machine learning to identify subpopulations of pediatric hemodialysis patients in an electronic health record database

McKnite, A. M., Job, K. M., Nelson, R. D., Sherwin, C. M. T., Watt, K., & Brewer, S. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.101104 · Cited by 1 · see citing works

Genome-wide association study of ACE inhibitor-induced cough implicates neuropeptides and shows genetic overlap with chronic dry coughPreprint

Coley, K., Shepherd, D. J., Packer, R. J., John, C., Free, R. C., Hollox, E. J., Wain, L. V., Tobin, M. D., & Batini, C. (2022) · medRxiv · doi:10.1101/2022.06.30.22277097 · Cited by 1 · see citing works

Avances en citometría de masas y aplicabilidad en patología digital para estudios clínico-traslacionales en oncología

Cereceda, K., Jorquera, R., & Villarroel‐Espíndola, F. (2021) · Advances in Laboratory Medicine / Avances en Medicina de Laboratorio · doi:10.1515/almed-2021-0051

What Is Informatics?

Chen, E. S. (2021) · Health informatics · doi:10.1007/978-3-030-70558-9_2

Developing A Risk Stratification Tool For Predicting Opioid-Related Respiratory Depression After Noncardiac SurgeryPreprint

Roy, S., Bruehl, S. P., Feng, X., Shotwell, M. S., de Ven, T. V., Shaw, A., & Kertai, M. D. (2022) · Research Square · doi:10.21203/rs.3.rs-1370010/v1

E-Health System and Telemedicine: An Overview and its Applications in Health Care and Medicine

Anand, R. V., Parthiban, H., Chelladurai, K. S., Christyraj, J. D. S., & Christyraj, J. R. S. S. (2022) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9781681089553122010006

Extracting research-quality phenotypes from electronic health records to support precision medicine

Wei, W., & Denny, J. C. (2015) · Genome Medicine · doi:10.1186/s13073-015-0166-y · Cited by 243 · see citing works

Electronic health records-driven phenotyping: challenges, recent advances, and perspectives

Pathak, J., Kho, A. N., & Denny, J. C. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002428 · Cited by 274 · see citing works

Leveraging Electronic Health Records for Phenotyping

Wilcox, A. B. (2014) · Health informatics · doi:10.1007/978-1-4471-4646-9_4 · Cited by 6 · see citing works

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 223 · see citing works

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 · see citing works