Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia

Simple item page
dc.citation.articleNumber15100
dc.citation.journalTitleScientific Reports
dc.citation.volumeNumber10
dc.contributor.authorTseng, Vincent W.-S.
dc.contributor.authorSano, Akane
dc.contributor.authorBen-Zeev, Dror
dc.contributor.authorBrian, Rachel
dc.contributor.authorCampbell, Andrew T.
dc.contributor.authorHauser, Marta
dc.contributor.authorKane, John M.
dc.contributor.authorScherer, Emily A.
dc.contributor.authorWang, Rui
dc.contributor.authorWang, Weichen
dc.contributor.authorWen, Hongyi
dc.contributor.authorChoudhury, Tanzeem
dc.date.accessioned2021-12-17T20:09:13Z
dc.date.available2021-12-17T20:09:13Z
dc.date.issued2020
dc.description.abstractSchizophrenia 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.
dc.identifier.citationTseng, Vincent W.-S., Sano, Akane, Ben-Zeev, Dror, et al.. "Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia." <i>Scientific Reports,</i> 10, (2020) Springer Nature: https://doi.org/10.1038/s41598-020-71689-1.
dc.identifier.digitals41598-020-71689-1
dc.identifier.doihttps://doi.org/10.1038/s41598-020-71689-1
dc.identifier.urihttps://hdl.handle.net/1911/111889
dc.language.isoeng
dc.publisherSpringer Nature
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleUsing behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
s41598-020-71689-1.pdf
Size:
1.87 MB
Format:
Adobe Portable Document Format
Download
Collections
Faculty Publications
ECE Publications