Assessment of Bipolar Disorder Using Heterogeneous Data of Smartphone-Based Digital Phenotyping

• Main Authors: Su, Hung-Yi, Wu, Chung-Hsien, Liou, Cheng-Ray, Lin, Esther Ching-Lan, See Chen, Po
• Format: Conference Proceeding
• Language: English
• Subjects: Bipolar disorder; Data mining; Data models; digital phenotype; ensemble method; Feature extraction; heterogeneous data; missing data; Predictive models; Signal processing; Training
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP39728.2021.9415008

In mental health disorder, Bipolar Disorder (BD) is one of the most common mental illness. Using rating scales for assessment is one of the approaches for diagnosing and tracking BD patients. However, the requirement for manpower and time is heavy in the process of evaluation. In order to reduce the cost of social and medical resources, this study collects the user's data by the App on smartphones, consisting of location data (GPS), self-report scales, daily mood, sleeping time and records of multi-media (text, speech, video) which are heterogeneous digital phenotyping data, to build a database. The features of each heterogeneous digital phenotyping data are extracted independently. Lasso Regression and ElasticNet Regression methods are employed to predict the score of Hamilton Depression Rating Scale (HAM-D) and Young Mania Rating Scale (YMRS), as a reference for the evaluation of BD. As incomplete and missing data are very common in medical research, the ensemble method is adopted to combine the results from different models trained with different combinations of missing data. The collected heterogeneous digital phenotyping data from 84 BD patients were used for training and evaluation of the proposed approach based on five-fold cross validation method. Experimental results show that the performance of the assessment system using the proposed method are encouraging.