论文标题
每日PHQ-2抑郁预测和预测的记录数据
Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting
论文作者
论文摘要
数字健康应用程序对于评估和监测患有精神健康状况（例如抑郁症）的人的福祉变得越来越重要。上述应用的一个普遍目标是预测自我评估的患者 - 健康问题（PHQ）的结果，表明抑郁症患者的当前症状严重程度。在这项工作中，我们探讨了使用积极收集的数据预测和预测新收集的纵向数据集上的PHQ-2分数的潜力。我们获得了最佳的1.417 MAE，用于每天预测PHQ-2分数，在使用的数据集中，该phq-2分数的范围为0到12，使用剩余的对象输出的交叉验证，以及最佳的1.914最佳MAE，用于预测PHQ-2得分，该数据使用数据从最近的7天到过去7天。这说明了通过将主动收集的数据纳入抑郁症监测应用程序中可以获得的添加价值。
Digital health applications are becoming increasingly important for assessing and monitoring the wellbeing of people suffering from mental health conditions like depression. A common target of said applications is to predict the results of self-assessed Patient-Health-Questionnaires (PHQ), indicating current symptom severity of depressive individuals. In this work, we explore the potential of using actively-collected data to predict and forecast daily PHQ-2 scores on a newly-collected longitudinal dataset. We obtain a best MAE of 1.417 for daily prediction of PHQ-2 scores, which specifically in the used dataset have a range of 0 to 12, using leave-one-subject-out cross-validation, as well as a best MAE of 1.914 for forecasting PHQ-2 scores using data from up to the last 7 days. This illustrates the additive value that can be obtained by incorporating actively-collected data in a depression monitoring application.
