Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study
Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study
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DOI：
10.2196/19348
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发表时间：
2020-09-01
影响因子：
5.2
通讯作者：
Kane, John M.
中科院分区：
文献类型：
--
作者：
Birnbaum, Michael Leo;Kulkarni, Prathamesh ''Param'';Kane, John M.
背景：精神病学几乎完全依赖于患者的自我报告，几乎没有客观可靠的测试或辅助信息来源来帮助诊断和评估程序。目的：我们旨在开发基于互联网搜索活动的计算算法，旨在支持精神分裂症谱系障碍患者的诊断程序和复发识别。方法：我们从42名精神分裂症谱系障碍患者和74名15-35岁(平均24.4岁，44.0%男性)的健康志愿者中提取了32,733个带有时间戳的搜索查询，并利用这些时间、频率和时间特征构建了机器学习诊断和复发分类器。结果：分类器预测精神分裂症谱系障碍的诊断，曲线下面积为0.74，预测精神分裂症谱系障碍患者的精神病复发，曲线下面积为0.71。与健康参与者相比，那些患有精神分裂症谱系障碍的人进行的搜索更少，他们的搜索由更少的单词组成。在再次住院之前，患有精神分裂症谱系障碍的参与者更有可能使用与听力、知觉和愤怒相关的词汇，而不太可能使用与健康相关的词汇。结论：在线搜索活动有望收集客观和容易获取的精神症状指标。利用搜索活动作为附带的行为健康信息，将代表着利用客观数字数据改善精神健康监测的努力的重大进展。
Background: Psychiatry is nearly entirely reliant on patient self-reporting, and 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 develop computational algorithms based on internet search activity designed to support diagnostic procedures and relapse identification in individuals with schizophrenia spectrum disorders.Methods: We extracted 32,733 time-stamped search queries across 42 participants with schizophrenia spectrum disorders and 74 healthy volunteers between the ages of 15 and 35 (mean 24.4 years, 44.0% male), and built machine-learning diagnostic and relapse classifiers utilizing the timing, frequency, and content of online search activity.Results: Classifiers predicted a diagnosis of schizophrenia spectrum disorders with an area under the curve value of 0.74 and predicted a psychotic relapse in individuals with schizophrenia spectrum disorders with an area under the curve of 0.71. Compared with healthy participants, those with schizophrenia spectrum disorders made fewer searches and their searches consisted of fewer words. Prior to a relapse hospitalization, participants with schizophrenia spectrum disorders were more likely to use words related to hearing, perception, and anger, and were 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 a major advancement in efforts to capitalize on objective digital data to improve mental health monitoring.