Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks.
Daniel A AdlerDror Ben-ZeevVincent Wen-Sheng TsengJohn M KaneRachel Marie BrianAndrew T CampbellMarta HauserEmily A SchererTanzeem ChoudhuryPublished in: JMIR mHealth and uHealth (2020)
Our proposed method predicted a higher rate of anomalies in patients with SSDs within the 30-day near relapse period and can be used to uncover individual-level behaviors that change before relapse. This approach will enable technologists and clinicians to build unobtrusive digital mental health tools that can predict incipient relapse in SSDs.