Mood Prediction for Bipolar Disorder Patient with Sleep Pattern Information

Dongju Lim

KAIST

"Mood Prediction for Bipolar Disorder Patient with Sleep Pattern Information"

Mood episode prediction is an essential task for the treatment of bipolar disorder patients. Recent studies revealed that sleep patterns and circadian rhythm misalignment are valuable information to predict mood episodes. However, the specific contributions of different sleep and circadian rhythm information to mood prediction are less understood. Here, we employed the XGBoost model and compare the importance of sleep and circadian rhythm features in predicting mood episodes. Additionally, we used SHAP value analysis to show the circadian rhythm and mood relationship difference between depressive episodes and hypomanic episodes.
Additional authors: Yun Min Song 1,2; Taek Lee 4; Heon Jeong Lee 3; Jae Kyoung Kim 1,2 1) Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, KOREA 2) Biomedical Mathematics Group, Institute for Basic Science, Daejeon 34126, KOREA 3) Korea University College of Medicine, Department of Psychiatry, Seoul 02841, KOREA 4) Sungshin University, Department of Convergence Security Engineering, Seoul 02844, KOREA

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