每日PHQ-2抑郁预测和预报的日志数据
Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting
摘要：
数字健康应用对于评估和监测患有抑郁症等精神健康状况的人的健康状况变得越来越重要。所述应用的共同目标是预测自我评估的患者健康量表（PHQ）的结果，指示抑郁个体的当前症状严重性。在这项工作中，我们探索了使用主动收集的数据来预测和预测新收集的纵向数据集上的每日PHQ-2评分的潜力。我们获得了每日预测PHQ-2评分的最佳MAE为1.417，特别是在所使用的数据集中，范围为0至12，使用leave-one-subject-out交叉验证，以及使用最多7天的数据预测PHQ-2评分的最佳MAE为1.914。这说明了可以通过将主动收集的数据合并到抑郁症监测应用程序中来获得的附加值。
英文摘要：
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.
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