ArticleThe American journal of psychiatry2015
Validation of electronic health record phenotyping of bipolar disorder cases and controls.
Article in The American journal of psychiatry, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 7 of them syntheses that pooled it.
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Who cites it
81 citing papers in PubMed, 7 syntheses or guidelines pooled it, 158 citations in OpenAlex.
- Clinical Characterization of Copy Number Variants Associated With Neurodevelopmental Disorders in a Large-scale Multiancestry Biobank.JAMA psychiatry · 2022Pooled it
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- Machine Learning and Natural Language Processing in Mental Health: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients Across Four Health Care Systems.The American journal of psychiatry · 2019Pooled it
- Genome-wide Association Study of Dimensional Psychopathology Using Electronic Health Records.Biological psychiatry · 2018Pooled it
- Extracting information from the text of electronic medical records to improve case detection: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2016Pooled it
- Electronic self-monitoring of mood using IT platforms in adult patients with bipolar disorder: A systematic review of the validity and evidence.BMC psychiatry · 2016Pooled it
- An electronic health record (EHR) phenotype algorithm to identify patients with attention deficit hyperactivity disorders (ADHD) and psychiatric comorbidities.Journal of neurodevelopmental disorders · 2022Trial
- Electronic Health Record-Based Phenotyping for Obsessive-Compulsive Disorder: Algorithm Development and Multicenter Validation Study.JMIR mental health · 2026Article
- Measuring Depression Severity With Clinical Global Impression-Severity Scale Scores From Clinical Notes Using Large Language Models: Validation Study.JMIR formative research · 2026Article
- Article
- Postpartum Psychosis: could genetic vulnerability to insomnia or short sleep duration be protective?Translational psychiatry · 2026Article
- Machine Learning in Adolescent Mental Health: Advanced Comorbidity Analysis and Text Mining Insights.Healthcare (Basel, Switzerland) · 2025Article
- Prediction of early-onset bipolar using electronic health records.Journal of child psychology and psychiatry, and allied disciplines · 2025Article
- Strategies for Creating Robust Patient Groups to Study Diverse Conditions with Electronic Health Records.Annual review of biomedical data science · 2025Review
- Research on the proximity relationships of psychosomatic disease knowledge graph modules extracted by large language models.Scientific reports · 2025Article
- Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research.Molecular psychiatry · 2025Review
- The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review.International journal of mental health nursing · 2025Review
- Biomedical literature-based clinical phenotype definition discovery using large language models.Database : the journal of biological databases and curation · 2025Article
- Predicting Conversion From Unipolar Depression to Bipolar Disorder and Schizophrenia: A 10-Year Retrospective Cohort Study on 12,182 Inpatients.Depression and anxiety · 2025Article
21 more citing papers are in PubMed but not listed here.
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Authors and funding
20 authors at 1 institution in 1 country.
Funding
Abstract
objectiveThe study was designed to validate use of electronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects.
methodEHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype diagnoses was calculated against diagnoses from direct semistructured interviews of 190 patients by trained clinicians blind to EHR diagnosis.
resultsThe PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR-classified control subject received a diagnosis of bipolar disorder on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based classifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses.
conclusionsSemiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.