biostudies-literatureFernandes MNCATS NIH HHSNCCDPHP CDC HHSCenters for Disease Control and PreventionNIA NIH HHSNHLBI NIH HHSNIH Clinical CenterNINDS NIH HHS1472-1481https://www.ebi.ac.uk/biostudies/studies/S-EPMC10239346biostudies-literatureUnknown64(6)<h4>Objective</h4>Unstructured data present in electronic health records (EHR) are a rich source of medical information; however, their abstraction is labor intensive. Automated EHR phenotyping (AEP) can reduce the need for manual chart review. We present an AEP model that is designed to automatically identify patients diagnosed with epilepsy.<h4>Methods</h4>The ground truth for model training and evaluation was captured from a combination of structured questionnaires filled out by physicians for a subset of patients and manual chart review using customized software. Modeling features included indicators of the presence of keywords and phrases in unstructured clinical notes, prescriptions for antiseizure medications (ASMs), International Classification of Diseases (ICD) codes for seizures and epilepsy, number of ASMs and epilepsy-related ICD codes, age, and sex. Data were randomly divided into training (70%) and hold-out testing (30%) sets, with distinct patients in each set. We trained regularized logistic regression and an extreme gradient boosting models. Model performance was measured using area under the receiver operating curve (AUROC) and area under the precision-recall curve (AUPRC), with 95% confidence intervals (CI) estimated via bootstrapping.<h4>Results</h4>Our study cohort included 3903 adults drawn from outpatient departments of nine hospitals between February 2015 and June 2022 (mean age = 47 ± 18 years, 57% women, 82% White, 84% non-Hispanic, 70% with epilepsy). The final models included 285 features, including 246 keywords and phrases captured from 8415 encounters. Both models achieved AUROC and AUPRC of 1 (95% CI = .99-1.00) in the hold-out testing set.<h4>Significance</h4>A machine learning-based AEP approach accurately identifies patients with epilepsy from notes, ICD codes, and ASMs. This model can enable large-scale epilepsy research using EHR databases.EpilepsiaIdentification of patients with epilepsy using automated electronic health records phenotyping.PMC10239346R01HL161253RF1 AG064312R01 NS107291RF1NS120947K08 AG0533805K08AG053380‐02R01 NS102574K23NS114201R01 NS126282R01 HL1612532P01AG032952‐115R01AG062282‐02R01NS107291R01 NS1021901R01AG073410‐01U48 DP006377UL1 TR002541R01 AG0734103R01AG062282‐03S1P01 AG032952R01NS102574R01AG073410R01NS102190RF1 NS120947R01 AG062282K23 NS114201RF1AG064312U48DP006377Jing JGe WMoura LMVRMcGraw CFernandes MWestover MBJacobs CZafar SFCardall AfalseIdentification of patients with epilepsy using automated electronic health records phenotyping.<h4>Objective</h4>Unstructured data present in electronic health records (EHR) are a rich source of medical information; however, their abstraction is labor intensive. Automated EHR phenotyping (AEP) can reduce the need for manual chart review. We present an AEP model that is designed to automatically identify patients diagnosed with epilepsy.<h4>Methods</h4>The ground truth for model training and evaluation was captured from a combination of structured questionnaires filled out by physicians for a subset of patients and manual chart review using customized software. Modeling features included indicators of the presence of keywords and phrases in unstructured clinical notes, prescriptions for antiseizure medications (ASMs), International Classification of Diseases (ICD) codes for seizures and epilepsy, number of ASMs and epilepsy-related ICD codes, age, and sex. Data were randomly divided into training (70%) and hold-out testing (30%) sets, with distinct patients in each set. We trained regularized logistic regression and an extreme gradient boosting models. Model performance was measured using area under the receiver operating curve (AUROC) and area under the precision-recall curve (AUPRC), with 95% confidence intervals (CI) estimated via bootstrapping.<h4>Results</h4>Our study cohort included 3903 adults drawn from outpatient departments of nine hospitals between February 2015 and June 2022 (mean age = 47 ± 18 years, 57% women, 82% White, 84% non-Hispanic, 70% with epilepsy). The final models included 285 features, including 246 keywords and phrases captured from 8415 encounters. Both models achieved AUROC and AUPRC of 1 (95% CI = .99-1.00) in the hold-out testing set.<h4>Significance</h4>A machine learning-based AEP approach accurately identifies patients with epilepsy from notes, ICD codes, and ASMs. This model can enable large-scale epilepsy research using EHR databases.2023-01-01T00:00:00Z2023 Jun2025-06-27T03:05:26.428Z2025-04-07T04:25:11.78ZS-EPMC102393463693431710.1111/epi.17589