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

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

SUBMITTER: Tseng VW 

PROVIDER: S-EPMC7492221 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature