ArticleJMIR mental health2020
Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study.
Article in JMIR mental health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review.BMJ open · 2025Pooled it
- Psychotic relapse prediction via biomarker monitoring: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Artificial Intelligence Applications in Mental Health: A Systematic Review of Clinical Practice, Educational Transformation, and Ethical Governance.Healthcare (Basel, Switzerland) · 2026Review
- A scoping review of the use of artificial intelligence as a psychological assessment tool.Translational psychiatry · 2026Article
- Using AI to Detect Psychosis Relapse: Scoping Review.JMIR mental health · 2026Article
- Computer-Assisted Performance-Based Assessment for Mental Health: A Scoping Review.PsyCh journal · 2026Article
- Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.Translational psychiatry · 2026Article
- Acceptability of Sharing Internet Browsing History for Cancer Research: Think-Aloud and Interview Study.JMIR cancer · 2026Article
- Promises and Pitfalls of Internet Search Data in Mental Health: Critical Review.JMIR mental health · 2025Review
- Harnessing Internet Search Data as a Potential Tool for Medical Diagnosis: Literature Review.JMIR mental health · 2025Review
- A prognostic model for predicting functional impairment in youth mental health services.European psychiatry : the journal of the Association of European Psychiatrists · 2024Article
- Classifying Schizophrenia Cases by Artificial Neural Network Using Japanese Web-Based Survey Data: Case-Control Study.JMIR formative research · 2023Article
- Cross-Platform Detection of Psychiatric Hospitalization via Social Media Data: Comparison Study.JMIR mental health · 2022Article
- Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment.Current psychiatry reports · 2022Review
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study.JMIR mHealth and uHealth · 2021Article
- Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer.JMIR medical informatics · 2021Review
- Clinical and Cost-Effectiveness of PSYCHOnlineTHERAPY: Study Protocol of a Multicenter Blended Outpatient Psychotherapy Cluster Randomized Controlled Trial for Patients With Depressive and Anxiety Disorders.Frontiers in psychiatry · 2021Article
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Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundPsychiatry 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.
objectiveWe aimed to develop computational algorithms based on internet search activity designed to support diagnostic procedures and relapse identification in individuals with schizophrenia spectrum disorders.
methodsWe 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.
resultsClassifiers 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.
conclusionsOnline 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.
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Registered trials
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