ArticleLearning health systems2020
Subphenotyping depression using machine learning and electronic health records.
Article in Learning health systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review.BMC medical informatics and decision making · 2023Pooled it
- Prevalence of fibromyalgia in asthma and its impact on asthma control: a case-control study.Annals of medicine · 2025Article
- Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.Journal of biomedical informatics · 2025Article
- Association of clinical phenotypes of depression with comorbid conditions, treatment patterns and outcomes: a 10-year region-based cohort study.Translational psychiatry · 2024Observational
- Identifying data-driven subtypes of major depressive disorder with electronic health records.Journal of affective disorders · 2024Article
- Determining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists.JAMIA open · 2023Article
- Establishment of a Public Mental Health Database for Research Purposes in the Ferrara Province: Development and Preliminary Evaluation Study.JMIR medical informatics · 2023Article
- Privacy-preserving patient clustering for personalized federated learning.Proceedings of machine learning research · 2023Article
- Comparing ascertainment of chronic condition status with problem lists versus encounter diagnoses from electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Desiderata for the development of next-generation electronic health record phenotype libraries.GigaScience · 2021Article
- Why does human phenomics matter today?Learning health systems · 2020Article
- Subphenotyping depression using machine learning and electronic health records.Learning health systems · 2020Article
- Screening for Depression, Anxiety, and Suicidality: Insights From Electronic Health Record Data in an Academic Medical Center.Journal of primary care & community healthArticle
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
objectiveTo identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications. MATERIALS AND
methodsUsing EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics.
resultsUsing the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. Anxiety and tobacco use were common comorbidities in this subphenotype.
conclusionComputationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.
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