ArticlePsychological medicine2012
Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model.
Article in Psychological medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers, 7 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
111 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled 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
- Can mental health diagnoses in administrative data be used for research? A systematic review of the accuracy of routinely collected diagnoses.BMC psychiatry · 2016Pooled it
- Text mining applications in psychiatry: a systematic literature review.International journal of methods in psychiatric research · 2016Pooled it
- An autism case history to review the systematic analysis of large-scale data to refine the diagnosis and treatment of neuropsychiatric disorders.Biological psychiatry · 2015Pooled it
- Safety of benzathine penicillin for preventing congenital syphilis: a systematic review.PloS one · 2013Pooled it
- Trial
- 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
- Phenotyping antidepressant treatment response with deep learning in electronic health records.Translational psychiatry · 2026Article
- Real‑World Clinical Characterization of Major Depressive Disorder and Treatment‑Resistant Depression Supported by Natural Language Processing: Multicenter Observational Study From the MOOD Project.Interactive journal of medical research · 2026Article
- Machine Learning in Adolescent Mental Health: Advanced Comorbidity Analysis and Text Mining Insights.Healthcare (Basel, Switzerland) · 2025Article
- Large Language Models for Psychiatric Phenotype Extraction from Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- Estimating depression severity in narrative clinical notes using large language models.Journal of affective disorders · 2025Article
- Research on the proximity relationships of psychosomatic disease knowledge graph modules extracted by large language models.Scientific reports · 2025Article
- Generalizability of Risk Models for Treatment-Resistant Depression Across Three Health Systems.medRxiv : the preprint server for health sciences · 2025Article
- Considerations and Challenges When Using Clinical and Vital Record Review for Suicide Research.Journal of patient safety · 2025Article
- Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification.BMC medical informatics and decision making · 2025Article
- Clinical associations with treatment resistance in depression: An electronic health record study.Psychiatry research · 2024Article
51 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
backgroundElectronic medical records (EMR) provide a unique opportunity for efficient, large-scale clinical investigation in psychiatry. However, such studies will require development of tools to define treatment outcome.
methodNatural language processing (NLP) was applied to classify notes from 127 504 patients with a billing diagnosis of major depressive disorder, drawn from out-patient psychiatry practices affiliated with multiple, large New England hospitals. Classifications were compared with results using billing data (ICD-9 codes) alone and to a clinical gold standard based on chart review by a panel of senior clinicians. These cross-sectional classifications were then used to define longitudinal treatment outcomes, which were compared with a clinician-rated gold standard.
resultsModels incorporating NLP were superior to those relying on billing data alone for classifying current mood state (area under receiver operating characteristic curve of 0.85-0.88 v. 0.54-0.55). When these cross-sectional visits were integrated to define longitudinal outcomes and incorporate treatment data, 15% of the cohort remitted with a single antidepressant treatment, while 13% were identified as failing to remit despite at least two antidepressant trials. Non-remitting patients were more likely to be non-Caucasian (p<0.001).
conclusionsThe application of bioinformatics tools such as NLP should enable accurate and efficient determination of longitudinal outcomes, enabling existing EMR data to be applied to clinical research, including biomarker investigations. Continued development will be required to better address moderators of outcome such as adherence and co-morbidity.
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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.