Automatic Bipolar Disorder Assessment Using Machine Learning With Smartphone-Based Digital Phenotyping

Abstract

Bipolar disorder (BD) is one of the most common mental illnesses worldwide. In this study, a smartphone application was developed to collect digital phenotyping data of users, and an ensemble method combining the results from a model pool was established through heterogeneous digital phenotyping. The aim was to predict the severity of bipolar symptoms by using two clinician-administered scales, the Hamilton Depression Rating Scale (HAM-D) and the Young Mania Rating Scale (YMRS). The collected digital phenotype data included the user's location information (GPS), self-report scales, daily mood, sleep patterns, and multimedia records (text, speech, and video). Each category of digital phenotype data was used for training models and predicting the rating scale scores (HAM-D and YMRS). Seven models were tested and compared, and different combinations of feature types were used to evaluate the performance of heterogeneous data. To address missing data, an ensemble approach was employed to increase flexibility in rating scale score prediction. This study collected heterogeneous digital phenotype data from 84 individuals with BD and 11 healthy controls. Five-fold cross-validation was employed for evaluation. The experimental results revealed that the Lasso and ElasticNet regression models were the most effective in predicting rating scale scores, and heterogeneous data performed better than homogeneous data, with a mean absolute error of 1.36 and 0.55 for HAM-D and YMRS, respectively; this margin of error meets medical requirements.