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

• Published in: Scientific reports 2020-09, Vol.10 (1), p.15100, Article 15100
• Main Authors: Tseng, Vincent W.-S., Sano, Akane, Ben-Zeev, Dror, Brian, Rachel, Campbell, Andrew T., Hauser, Marta, Kane, John M., Scherer, Emily A., Wang, Rui, Wang, Weichen, Wen, Hongyi, Choudhury, Tanzeem
• Format: Article
• Language: English
• Subjects: 692/308; 692/700; Adolescent; Behavior - physiology; Cluster Analysis; Female; Humanities and Social Sciences; Humans; Learning - physiology; Machine Learning; Male; multidisciplinary; Schizophrenia - diagnosis; Schizophrenia - physiopathology; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-71689-1

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.