Subphenotyping depression using machine learning and electronic health records.
Zhenxing XuFei WangPrakash AdekkanattuBudhaditya BoseVeer VekariaPascal S BrandtGuoqian JiangRichard C KieferYuan LuoJennifer A PachecoLuke V RasmussenJie XuGeorge AlexopoulosJyotishman PathakPublished in: Learning health systems (2020)
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.