Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia
Open accessAbstract
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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Inherent in these data sets are behavioral patterns: routines, rhythms, activities, and interactions that are useful in complementing traditional depression assessment methods, in studying the mental health of individuals, and in developing timely mental health interventions.
“Inherent in these data sets are behavioral patterns: routines, rhythms, activities, and interactions that are useful in complementing traditional depression assessment methods, in studying the mental health of individuals, and in developing timely mental health interventions [14,22,25-28].”
Passively sensed participants' smartphone screen interaction (on and off states) behavior was demonstrated to be an important predictor of mental health.
“Passively sensed participants’ smartphone screen interaction (ie, on and off states) behavior was demonstrated to be an important predictor of mental health [27].”
The findings in this study are supported by prior research that investigated the relationship between screen interactions and mental health.
“The findings in this study are also supported by prior research that investigated the relationship between screen interactions and mental health [3,27,28,33].”
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Self-citation
- Authors
- Tseng, V. W.-S., Sano, A., Ben-Zeev, D., Brian, R., Campbell, A. T., Hauser, M., Kane, J. M., Scherer, E. A., Wang, R., Wang, W., Wen, H., & Choudhury, T.
- Affiliations
- Cornell University (US)
- Rice University (US)
- University of Washington (US)
- Dartmouth College (US)
- Vanguard (United States) (US)
- Donald & Barbara Zucker School of Medicine at Hofstra/Northwell (US)
- Meta (United States) (US)
- Corresponding author
- Tseng, Vincent W.-S.
- ORCID
- Venue
- Scientific Reports
- Published
- 2020-09-15
- Volume
- 10
- Issue
- 1
- Pages
- 15100-15100
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7492221
- OpenAlex
- W3087590708
- Cited by
- 44
- References
- 68
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 8 · 2023: 11 · 2022: 10 · 2021: 4
- FWCI
- 6.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Rice Research Repository (Rice University) (submittedVersion, cc-by)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (R01MH103148)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Keywords
- schizophrenia, behavioral rhythms, sleep rhythm, smartphone sensing, multi-task learning, ecological momentary assessment, symptom prediction, interpretable models, symptom subtypes, early detection, mental health monitoring
- MeSH terms
- Machine Learning, Adolescent, Behavior, Female, Humans, Learning, Male, Schizophrenia, Cluster Analysis
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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