Predicting symptom trajectories of schizophrenia using mobile sensing

Wang, Rui, Scherer, Emily A., Walsh, Megan, Wang, Weichen, Aung, Min Hane, Ben-zeev, Dror, Brian, Rachel, Campbell, Andrew T., Choudhury, Tanzeem, Hauser, Marta and Kane, John (2018) Predicting symptom trajectories of schizophrenia using mobile sensing. GetMobile: Mobile Computing and Communications, 22 (2). pp. 32-37. ISSN 2375-0529

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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.

Item Type:	Article
Related URLs:	http://dl.acm.org/citation.cfm?doid=3276...
Depositing User:	LivePure Connector
Date Deposited:	17 Oct 2019 15:30
Last Modified:	09 Jun 2022 14:41
URI:	https://ueaeprints.uea.ac.uk/id/eprint/72681
DOI:

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