We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Subphenotyping depression using machine learning and electronic health records.
- Authors
Xu, Zhenxing; Wang, Fei; Adekkanattu, Prakash; Bose, Budhaditya; Vekaria, Veer; Brandt, Pascal; Jiang, Guoqian; Kiefer, Richard C.; Luo, Yuan; Pacheco, Jennifer A.; Rasmussen, Luke V.; Xu, Jie; Alexopoulos, George; Pathak, Jyotishman
- Abstract
Objective: To 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 Methods: Using 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. Results: Using 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. Conclusion: Computationally 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.
- Subjects
ELECTRONIC health records; EDUCATIONAL technology; MACHINE learning; EXPERIMENTAL design; COMORBIDITY
- Publication
Learning Health Systems, 2020, Vol 4, Issue 4, p1
- ISSN
2379-6146
- Publication type
Article
- DOI
10.1002/lrh2.10241