Vahedifard Farzan, Birmaher Boris, Iyengar Satish, Wolfe Maria, Lepore Brianna N, Chobany Mariah, Abdul-Waalee Halimah, Malgireddy Greeshma, Hart Jonathan A, Bertocci Michele A, Diler Rasim S
Department of Psychiatry, University of Pittsburgh School of Medicine, USA.
UPMC Western Psychiatric Hospital, USA.
Psychiatry Res Commun. 2025 Jun;5(2). doi: 10.1016/j.psycom.2025.100212. Epub 2025 Apr 17.
Measures of daily activity may be objective markers to help differentiate adolescent bipolar disorder (BD). We used chart reviewed actigraphy data collected from 2014 to 2023, and AI methods to classify well-characterized inpatient adolescents diagnosed with --), --, --, and other diagnoses (). 389 inpatient adolescents (232 female, mean age 15.07), wore an actigraphy monitor for the duration of their inpatient stay (mean number of unique days = 13.04 days). Activity was characterized into four 60-min maximum and minimum daily activity bins, automatically identified using a novel Python script. Feature engineering further described time-series data. 5193 days of data were split into training and testing sets. Random Forest and XGBoost models were trained with cross-validation on the training set and model metrics were compared. The best models were tested on the testing set. XGBoost with feature selection provided the most robust and balanced classification model. The most influential feature was the engineered difference between peak active hours, which along with other activity and age features classified all diagnostic groups with 91.5 % accuracy. Results indicated that daily activity levels, especially the variability between peak activity hours, showed potential for improving diagnostic precision in psychiatric settings. Actigraphy, combined with machine learning, offers a promising approach for classifying diagnostic groups among inpatient adolescent populations and engineered maximum and minimum hourly activity features may provide objective markers to improve diagnostic accuracy. Future studies should aim to test and validate these findings and assess their clinical implications in larger, diverse cohorts in the natural environment.
日常活动量的测量可能是有助于区分青少年双相情感障碍（BD）的客观指标。我们使用了2014年至2023年收集的经图表审核的活动记录仪数据，并采用人工智能方法对诊断明确的住院青少年进行分类，这些青少年被诊断为——、——、——以及其他诊断（）。389名住院青少年（232名女性，平均年龄15.07岁）在住院期间佩戴活动记录仪（平均独特天数 = 13.04天）。活动被分为四个每日活动量的最大和最小60分钟区间，使用一个新颖的Python脚本自动识别。特征工程进一步描述了时间序列数据。5193天的数据被分为训练集和测试集。随机森林和XGBoost模型在训练集上进行交叉验证训练，并比较模型指标。最佳模型在测试集上进行测试。带有特征选择的XGBoost提供了最稳健和平衡的分类模型。最具影响力的特征是峰值活跃小时数之间的工程差异，该特征与其他活动和年龄特征一起以91.5%的准确率对所有诊断组进行了分类。结果表明，日常活动水平，尤其是峰值活动小时数之间的变异性，在精神科环境中显示出提高诊断准确性的潜力。活动记录仪与机器学习相结合，为住院青少年人群中的诊断组分类提供了一种有前景的方法，并且工程化的每小时最大和最小活动特征可能提供客观指标以提高诊断准确性。未来的研究应旨在测试和验证这些发现，并评估它们在自然环境中更大、更多样化队列中的临床意义。
