Wu Chia-Tung, Hsieh Ming H, Chen I-Ming, Jhao Lian-Yin, Liu Ding-Shan, Wang Ssu-Ming, Wu Chia-Ting, Chien Yi-Ling
Master Program in Transdisciplinary Long-term Care and Management, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Department of Psychiatry, National Taiwan University Hospital, No.7, Chung-Shan South Road, Taipei, 10002, Taiwan, 886 2-23123456 ext 266013.
JMIR Med Inform. 2025 Sep 16;13:e66277. doi: 10.2196/66277.
Bipolar disorder (BD) is a highly recurrent disorder. Early detection, early intervention, and prevention of recurrent bipolar mood symptoms are key to a better prognosis.
This study aims to build prediction models for BD with machine learning algorithms.
This study recruited 24 participants with BD. The Beck Depression Inventory and Young Mania Rating Scale were used to evaluate depressive and manic episodes, respectively. Using digital biomarkers collected from wearable devices as input, 6 machine learning algorithms (logistic regression, decision tree, k-nearest neighbors, random forest, adaptive boosting, and Extreme Gradient Boosting) were used to build predictive models.
The prediction model for depressive symptoms achieved 83% accuracy, an area under the receiver operating characteristic curve (AUROC) of 0.89, and an F1-score of 0.65 on testing data. The prediction model for manic symptoms achieved 91% accuracy, an AUROC of 0.88, and an F1-score of 0.25 on testing data. With the interpretable model Shapley Additive Explanations, we found that relatively high resting heart rate, low activity, and lack of sleep may predict depressive symptoms.
This study demonstrated that digital biomarkers could be used to predict depressive and manic symptoms. This prediction model may be beneficial for the early detection of mood symptoms, facilitating timely treatment and helping to prevent BD recurrence.
双相情感障碍（BD）是一种高复发性疾病。早期发现、早期干预以及预防双相情感症状复发是改善预后的关键。
本研究旨在利用机器学习算法建立双相情感障碍的预测模型。
本研究招募了24名双相情感障碍患者。分别使用贝克抑郁量表和杨氏躁狂评定量表评估抑郁发作和躁狂发作。以从可穿戴设备收集的数字生物标志物作为输入，使用6种机器学习算法（逻辑回归、决策树、k近邻、随机森林、自适应增强和极端梯度提升）建立预测模型。
抑郁症状预测模型在测试数据上的准确率达到83%，受试者工作特征曲线下面积（AUROC）为0.89，F1分数为0.65。躁狂症状预测模型在测试数据上的准确率达到91%，AUROC为0.88，F1分数为0.25。通过可解释模型Shapley加性解释，我们发现静息心率相对较高、活动量低和睡眠不足可能预示着抑郁症状。
本研究表明数字生物标志物可用于预测抑郁和躁狂症状。这种预测模型可能有助于早期发现情绪症状，促进及时治疗并有助于预防双相情感障碍复发。
