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Keyword

computable phenotypes

Description: Algorithmic definitions of clinical conditions or traits that identify individuals from electronic health records or other structured health data.
OpenAlex ID: keywords/computable-phenotypes
Finding Our Way through Phenotypes
2015 · Andrew R. Deans, Suzanna E. Lewis, et al. · PLoS Biology · 204
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Considerations for Integration of Perioperative Electronic Health Records Across Institutions for Research and Quality Improvement: The Approach Taken by the Multicenter Perioperative Outcomes Group
2020 · Douglas A. Colquhoun, Amy M. Shanks, et al. · Anesthesia & Analgesia · 152
Functional Outcome After Intracranial Pressure Monitoring for Children With Severe Traumatic Brain Injury
2017 · Tellen D. Bennett, Peter E. DeWitt, et al. · Archives of Pediatrics and Adolescent Medicine · 84
Long COVID Incidence Proportion in Adults and Children Between 2020 and 2024: An Electronic Health Record-Based Study From the RECOVER Initiative
2025 · Hannah Mandel, Yun J Yoo, et al. · Clinical Infectious Diseases · 65
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Machine learning derivation of four computable 24-h pediatric sepsis phenotypes to facilitate enrollment in early personalized anti-inflammatory clinical trials
2022 · Yidi Qin, Kate F. Kernan, et al. · Critical Care · 58
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Identification of Incident Atrial Fibrillation From Electronic Medical Records
2022 · Alanna M. Chamberlain, Véronique L. Roger, et al. · Journal of the American Heart Association · 51
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Using a Multi-Institutional Pediatric Learning Health System to Identify Systemic Lupus Erythematosus and Lupus Nephritis
2021 · Scott E. Wenderfer, Joyce C. Chang, et al. · Clinical Journal of the American Society of Nephrology · 46
Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record–Based Computable Phenotype Derivation and Validation Study
2019 · David Chartash, Hyung Paek, et al. · JMIR Medical Informatics · 46
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Using and improving distributed data networks to generate actionable evidence: the case of real-world outcomes in the Food and Drug Administration’s Sentinel system
2020 · Jeffrey S Brown, Judith C Maro, et al. · Journal of the American Medical Informatics Association · 45
Electronic medical record–based cohort selection and direct-to-patient, targeted recruitment: early efficacy and lessons learned
2019 · Hailey N Miller, Kelly T Gleason, et al. · Journal of the American Medical Informatics Association · 45
Using Electronic Health Record Data to Rapidly Identify Children with Glomerular Disease for Clinical Research
2019 · Michelle R. Denburg, Hanieh Razzaghi, et al. · Journal of the American Society of Nephrology · 43
Interrupted Time Series of User‐centered Clinical Decision Support Implementation for Emergency Department–initiated Buprenorphine for Opioid Use Disorder
2020 · Wesley C. Holland, Bidisha Nath, et al. · Academic Emergency Medicine · 42
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Identifying heart failure using EMR-based algorithms
2018 · Geoffrey H. Tison, Alanna M. Chamberlain, et al. · International Journal of Medical Informatics · 42
Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients
2020 · Brihat Sharma, Dmitriy Dligach, et al. · BMC Medical Informatics and Decision Making · 40
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A case study evaluating the portability of an executable computable phenotype algorithm across multiple institutions and electronic health record environments
2018 · Jennifer A Pacheco, Luke V Rasmussen, et al. · Journal of the American Medical Informatics Association · 39
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A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry
2017 · Alon Geva, Jessica L. Gronsbell, et al. · The Journal of Pediatrics · 36
Computable Phenotype Implementation for a National, Multicenter Pragmatic Clinical Trial
2020 · Faraz S. Ahmad, Iben M. Ricket, et al. · Circulation Cardiovascular Quality and Outcomes · 34
Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering
2018 · Ming Tang, Chao Gao, et al. · Neuroinformatics · 34
Performance of a computable phenotype for identification of patients with diabetes within PCORnet: The Patient‐Centered Clinical Research Network
2019 · Andrew D. Wiese, Christianne L. Roumie, et al. · Pharmacoepidemiology and Drug Safety · 33
Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network
2019 · Ning Shang, Cong Liu, et al. · Journal of Biomedical Informatics · 32
A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning.
2018 · Majid Afshar, Cara Joyce, et al. · PubMed · 32
Long COVID Risk and Pre-COVID Vaccination: An EHR-Based Cohort Study from the RECOVER Program
2022 · M Daniel Brannock, Robert F Chew, et al. · medRxiv · 31
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Identification and Validation of a Sickle Cell Disease Cohort Within Electronic Health Records
2016 · Daniel E. Michalik, Bradley W. Taylor, et al. · Academic Pediatrics · 31
Development and Prospective Validation of Tools to Accurately Identify Neurosurgical and Critical Care Events in Children With Traumatic Brain Injury*
2017 · Tellen D. Bennett, Peter E. DeWitt, et al. · Pediatric Critical Care Medicine · 30
Development and evaluation of a computable phenotype to identify pediatric patients with leukemia and lymphoma treated with chemotherapy using electronic health record data
2019 · Charles A. Phillips, Hanieh Razzaghi, et al. · Pediatric Blood & Cancer · 29
Works count: 347
Citation count: 2,521
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