Subphenotyping depression using machine learning and electronic health records

Zhenxing Xu, Fei Wang, Prakash Adekkanattu, Budhaditya Bose, Veer Vekaria, Pascal Brandt, Guoqian Jiang, Richard C. Kiefer, Yuan Luo, Jennifer A. Pacheco, Luke V. Rasmussen, Jie Xu, George Alexopoulos, Jyotishman D Pathak

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalLearning Health Systems
DOIs
StateAccepted/In press - 2020

Keywords

  • depression
  • electronic health records
  • machine learning
  • phenotyping

ASJC Scopus subject areas

  • Health Informatics
  • Public Health, Environmental and Occupational Health
  • Health Information Management

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  • Cite this

    Xu, Z., Wang, F., Adekkanattu, P., Bose, B., Vekaria, V., Brandt, P., Jiang, G., Kiefer, R. C., Luo, Y., Pacheco, J. A., Rasmussen, L. V., Xu, J., Alexopoulos, G., & Pathak, J. D. (Accepted/In press). Subphenotyping depression using machine learning and electronic health records. Learning Health Systems. https://doi.org/10.1002/lrh2.10241