Accepted for/Published in: JMIR Mental Health
Date Submitted: Apr 15, 2020
Date Accepted: Jul 23, 2020
Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals with Early Psychosis: Feasibility Study
ABSTRACT
Background:
Psychiatry is nearly entirely reliant on patient self-report. There are few objective and reliable tests or sources of collateral information available to help diagnostic and assessment procedures. Technology offers opportunities to collect objective digital data to complement patient experience and facilitate more informed treatment decisions.
Objective:
We aimed to explore the feasibility of using collateral online search activity to support the diagnostic process and relapse detection in individuals with SSD.
Methods:
We extracted 32,733 time stamped search queries across 42 participants with schizophrenia spectrum disorders (SSD) and 74 healthy volunteers (HV) and built machine learning diagnostic and relapse classifiers based on the timing, frequency, and content of online search activity.
Results:
Classifiers predicted a diagnosis of SSD with an area under the curve (AUC) of 0.74 and predicted a psychotic relapse in individuals with SSD with an AUC of 0.71. Compared to healthy participants, those with SSD made fewer searches and their searches consisted of fewer words. Prior to a relapse hospitalization, participants with SSD were more likely to use words related to hearing, perception, and anger, and less likely to use words related to health.
Conclusions:
Online search activity holds promise for gathering objective and easily accessed indicators of psychiatric symptoms. Utilizing search activity as collateral behavioral health information would represent major advancement in efforts to capitalize on objective digital data to improve mental health monitoring. Clinical Trial: N/A
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