ArticleScientific reports2020
Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches.Sensors (Basel, Switzerland) · 2024Pooled it
- Beyond composite scores in chronotype assessment: item-level predictive patterns in the Morningness-Eveningness Questionnaire.Scientific reports · 2026Observational
- Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.Translational psychiatry · 2026Article
- Exploring the clinical utility of rhythmic digital markers for schizophrenia.PLOS digital health · 2025Article
- Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review.Journal of medical Internet research · 2025Article
- Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review.JMIR AI · 2025Review
- Forecasting mental states in schizophrenia using digital phenotyping data.PLOS digital health · 2025Article
- Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study.Journal of medical Internet research · 2024Article
- Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study.JMIR research protocols · 2024Article
- Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.Npj mental health research · 2024Article
- Introducing a new severity specifier for schizophrenia: conceptual framework and clinical implications.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2024Article
- Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research.Environment international · 2023Article
- Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2023Article
- Burnout and the Quantified Workplace: Tensions around Personal Sensing Interventions for Stress in Resident Physicians.Proceedings of the ACM on human-computer interaction · 2022Article
- First-Gen Lens: Assessing Mental Health of First-Generation Students across Their First Year at College Using Mobile Sensing.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2022Article
- Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint.Journal of medical Internet research · 2022Article
- Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis.JMIR mHealth and uHealth · 2022Article
- A call for open data to develop mental health digital biomarkers.BJPsych open · 2022Article
- Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies.PloS one · 2022Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Schizophrenia is a severe and complex psychiatric disorder with heterogeneous and dynamic multi-dimensional symptoms. Behavioral rhythms, such as sleep rhythm, are usually disrupted in people with schizophrenia. As such, behavioral rhythm sensing with smartphones and machine learning can help better understand and predict their symptoms. Our goal is to predict fine-grained symptom changes with interpretable models. We computed rhythm-based features from 61 participants with 6,132 days of data and used multi-task learning to predict their ecological momentary assessment scores for 10 different symptom items. By taking into account both the similarities and differences between different participants and symptoms, our multi-task learning models perform statistically significantly better than the models trained with single-task learning for predicting patients' individual symptom trajectories, such as feeling depressed, social, and calm and hearing voices. We also found different subtypes for each of the symptoms by applying unsupervised clustering to the feature weights in the models. Taken together, compared to the features used in the previous studies, our rhythm features not only improved models' prediction accuracy but also provided better interpretability for how patients' behavioral rhythms and the rhythms of their environments influence their symptom conditions. This will enable both the patients and clinicians to monitor how these factors affect a patient's condition and how to mitigate the influence of these factors. As such, we envision that our solution allows early detection and early intervention before a patient's condition starts deteriorating without requiring extra effort from patients and clinicians.
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