Digital Phenotyping-Based Bipolar Disorder Assessment Using Multiple Correlation Data Imputation and Lasso-MLP

Jia Hao Hsu, Chung Hsien Wu, Wei Kai Wang, Hung Yi Su, Esther Ching Lan Lin, Po See Chen

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)


Clinical rating scales can be used to assess the severity of bipolar disorder; however, their use involves clinician–patient interactions, which is labor-intensive. Therefore, this study proposes a digital-phenotyping-based system that provides clinical ratings of bipolar disorder severity using global positioning system, self-scale, daily mood, user emotion, sleep time, and multimedia data; these ratings are given on Hamilton Depression Rating Scale (HAM-D) and Young Mania Rating Scale (YMRS). A K-nearest-neighbor-based imputation method was used to handle missing data. In this method, missing data points are filled in with the multiple correlations between different features. Furthermore, the Least Absolute Shrinkage and Selection Operator (Lasso)-regression-based multilayer perceptron (Lasso-MLP) method was adopted to predict the total and factor scores on the HAM-D and YMRS. Five-fold cross-validation were used in evaluation experiments. When the designed data imputation method was used with Lasso-MLP, the mean square errors of the total score and average factor score on HAM-D (the YMRS) were 0.56 (0.38) and 1.88 (0.98), respectively, which were smaller than the corresponding values obtained through Lasso regression (by 0.12 and 0.05, respectively, for HAM-D and by 0.12 and 0.10, respectively, for the YMRS). The experimental results also indicated that the models trained with the imputed data outperformed those trained without imputed data. Thus, the developed approaches can eliminate the missing data problem and provide accurate clinical ratings.

頁(從 - 到)1-14
期刊IEEE Transactions on Affective Computing
出版狀態Accepted/In press - 2023

All Science Journal Classification (ASJC) codes

  • 軟體
  • 人機介面


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