We present a machine learning approach that uses data from smartphones and fitness trackers of 138 college students to identify students that experienced depressive symptoms at the end of the semester and students whose depressive symptoms worsened over the semester. Our novel approach is a feature extraction technique that allows us to select meaningful features indicative of depressive symptoms from longitudinal data. It allows us to detect the presence of post-semester depressive symptoms with an accuracy of 85.7% and change in symptom severity with an accuracy of 85.4%. It also predicts these outcomes with an accuracy of >80%, 11–15 weeks before the end of the semester, allowing ample time for pre-emptive interventions. Our work has significant implications for the detection of health outcomes using longitudinal behavioral data and limited ground truth. By detecting change and predicting symptoms several weeks before their onset, our work also has implications for preventing depression.

中文翻译：

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使用被动感知捕获的纵向症状检测抑郁症并预测其发作

我们提出了一种机器学习方法，该方法使用来自 138 名大学生的智能手机和健身追踪器的数据来识别在学期末出现抑郁症状的学生和抑郁症状在学期内恶化的学生。我们的新方法是一种特征提取技术，它允许我们从纵向数据中选择指示抑郁症状的有意义的特征。它使我们能够以 85.7% 的准确度检测学期后抑郁症状的存在，并以 85.4% 的准确度检测症状严重程度的变化。它还可以在学期结束前 11-15 周以 >80% 的准确度预测这些结果，从而为先发制人的干预留出充足的时间。我们的工作对使用纵向行为数据和有限的基本事实检测健康结果具有重要意义。通过在症状发作前几周检测变化和预测症状，我们的工作也对预防抑郁症产生了影响。