Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks
JMIR mhealth and uhealth2020Vol. 8(8), pp. e19962–e19962
Citations Over TimeTop 10% of 2020 papers
Daniel A. Adler, Dror Ben‐Zeev, Vincent W.-S. Tseng, John M. Kane, Rachel Brian, Andrew T. Campbell, Marta Hauser, Emily A. Scherer, Tanzeem Choudhury
Abstract
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.
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