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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 Pathak
Published 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.
Keyphrases
  • electronic health record
  • clinical trial
  • depressive symptoms
  • sleep quality
  • clinical decision support
  • open label
  • double blind
  • phase iii