用手机定位数据预测抑郁治疗效果，跨平台数据融合提升准确率
Cross-platform Prediction of Depression Treatment Outcome Using Location Sensory Data on Smartphones
- 通过领域自适应将安卓与苹果数据映射到同一特征空间
- 结合定位数据和初始问卷得分，F1最高达0.67
- 无需频繁填写问卷，适合长期心理状态监测
目前抑郁症治疗依赖对患者治疗反应的密切监测与适时调整治疗方案。然而，使用自评或医生评估量表进行监测存在负担重、成本高及回忆偏差等问题。本文探索利用智能手机被动采集的位置传感数据预测治疗结局。针对安卓与苹果两大主流平台间数据异构问题，采用领域自适应技术将数据映射至统一特征空间，并联合训练机器学习模型。结果表明，该方法显著优于无领域自适应的情况。此外，仅使用位置特征与基线自评量表得分，即可实现最高F1分数0.67，接近定期自评量表的效果，表明位置数据在预测抑郁症治疗结局方面具有广阔前景。
原文摘要 · Abstract (English)
Currently, depression treatment relies on closely monitoring patients response to treatment and adjusting the treatment as needed. Using self-reported or physician-administrated questionnaires to monitor treatment response is, however, burdensome, costly and suffers from recall bias. In this paper, we explore using location sensory data collected passively on smartphones to predict treatment outcome. To address heterogeneous data collection on Android and iOS phones, the two predominant smartphone platforms, we explore using domain adaptation techniques to map their data to a common feature space, and then use the data jointly to train machine learning models. Our results show that this domain adaptation approach can lead to significantly better prediction than that with no domain adaptation. In addition, our results show that using location features and baseline self-reported questionnaire score can lead to F1 score up to 0.67, comparable to that obtained using periodic self-reported questionnaires, indicating that using location data is a promising direction for predicting depression treatment outcome.
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