双相情感障碍中社交节律的自动检测。
Automatic detection of social rhythms in bipolar disorder.
作者信息
Abdullah Saeed, Matthews Mark, Frank Ellen, Doherty Gavin, Gay Geri, Choudhury Tanzeem
机构信息
Information Science, Gates Hall, Cornell University, Ithaca, NY 14853, USA
Information Science, Gates Hall, Cornell University, Ithaca, NY 14853, USA.
出版信息
J Am Med Inform Assoc. 2016 May;23(3):538-43. doi: 10.1093/jamia/ocv200. Epub 2016 Mar 14.
OBJECTIVE
To evaluate the feasibility of automatically assessing the Social Rhythm Metric (SRM), a clinically-validated marker of stability and rhythmicity for individuals with bipolar disorder (BD), using passively-sensed data from smartphones.
METHODS
Seven patients with BD used smartphones for 4 weeks passively collecting sensor data including accelerometer, microphone, location, and communication information to infer behavioral and contextual patterns. Participants also completed SRM entries using a smartphone app.
RESULTS
We found that automated sensing can be used to infer the SRM score. Using location, distance traveled, conversation frequency, and non-stationary duration as inputs, our generalized model achieves root-mean-square-error of 1.40, a reasonable performance given the range of SRM score (0-7). Personalized models further improve performance with mean root-mean-square-error of 0.92 across users. Classifiers using sensor streams can predict stable (SRM score ≥3.5) and unstable (SRM score <3.5) states with high accuracy (precision: 0.85 and recall: 0.86).
CONCLUSIONS
Automatic smartphone sensing is a feasible approach for inferring rhythmicity, a key marker of wellbeing for individuals with BD.
目的
利用智能手机的被动感知数据，评估自动评估社会节律指标（SRM）的可行性，SRM是双相情感障碍（BD）患者稳定性和节律性的临床验证指标。
方法
7名BD患者使用智能手机4周，被动收集包括加速度计、麦克风、位置和通信信息在内的传感器数据，以推断行为和情境模式。参与者还使用智能手机应用程序完成SRM记录。
结果
我们发现自动感知可用于推断SRM分数。以位置、行进距离、对话频率和非静止持续时间为输入，我们的广义模型实现了均方根误差为1.40，鉴于SRM分数范围（0 - 7），这是一个合理的性能。个性化模型进一步提高了性能，用户间的平均均方根误差为0.92。使用传感器数据流的分类器可以高精度地预测稳定（SRM分数≥3.5）和不稳定（SRM分数<3.5）状态（精确率：0.85，召回率：0.86）。
结论
智能手机自动感知是推断节律性的一种可行方法，节律性是BD患者幸福感的关键指标。
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