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Prediction of lithium response using clinical data.

Abraham NunesR ArdauA BerghöferA BocchettaC ChillottiV DeianaJ GarnhamE GrofT HajekMirko ManchiaB Müller-OerlinghausenM PinnaClaudia PisanuC O'DonovanG SeverinoC SlaneyA SuwalskaP ZvolskyP CervantesM Del ZompoP GrofJ RybakowskiL TondoT TrappenbergMartin Alda
Published in: Acta psychiatrica Scandinavica (2019)
Clinical data can inform out-of-sample LR prediction to a potentially clinically relevant degree. Despite the relevance of clinical course and the absence of rapid cycling, there was substantial between-site heterogeneity with respect to feature importance. Future work must focus on improving classification of true positives, better characterizing between- and within-site heterogeneity, and further testing such models on new external datasets.
Keyphrases
  • machine learning
  • electronic health record
  • deep learning
  • single cell
  • big data
  • current status
  • high intensity
  • artificial intelligence