Predicting symptom trajectories of schizophrenia using mobile sensing

Rui Wang, Emily A. Scherer, Megan Walsh, Weichen Wang, Min Hane Aung, Dror Ben-zeev, Rachel Brian, Andrew T. Campbell, Tanzeem Choudhury, Marta Hauser, John Kane

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Abstract

Continuously monitoring schizophrenia patients' psychiatric symptoms is crucial for in-time intervention and treatment adjustment. The Brief Psychiatric Rating Scale (BPRS) is a survey administered by clinicians to evaluate symptom severity in schizophrenia. The CrossCheck symptom prediction system is capable of tracking schizophrenia symptoms as measured by BPRS using passive sensing from mobile phones. We present results from a randomized control trial, where passive sensing data, self-reports, and clinician administered 7-item BPRS surveys are collected from 36 outpatients with schizophrenia. We show that our system can predict a symptom scale score based on a 7-item BPRS within +1.45 error on average. Finally, we discuss how well our predictive system reflects symptoms experienced by patients by reviewing a case study.
Original languageEnglish
Pages (from-to)32-37
Number of pages6
JournalGetMobile: Mobile Computing and Communications
Volume22
Issue number2
Publication statusPublished - 5 Sep 2018
Externally publishedYes

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