Automatic detection of social rhythms in bipolar disorder
Automatic detection of social rhythms in bipolar disorder
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DOI：
10.1093/jamia/ocv200
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发表时间：
2016-05-01
影响因子：
6.4
通讯作者：
Choudhury, Tanzeem
中科院分区：
文献类型：
--
作者：
Abdullah, Saeed;Matthews, Mark;Choudhury, Tanzeem
目的评估自动评估社会节律指标（SRM）的可行性，一个临床验证的标志物的稳定性和节律性的个人与双相情感障碍（BD），使用被动感知的数据从智能手机，7例BD患者使用智能手机4周被动收集传感器数据，包括加速度计，麦克风，位置和通信信息，以推断行为和上下文模式。参与者还完成了SRM条目使用智能手机应用程序。结果我们发现，自动传感可以用来推断SRM分数。使用位置，行驶距离，会话频率和非平稳持续时间作为输入，我们的广义模型实现了均方根误差为1.40，这是SRM得分范围（0-7）的合理性能。个性化模型进一步提高了性能，跨用户的平均均方根误差为0.92。使用传感器流的分类器可以预测稳定（SRM得分>= 3.5）和不稳定（SRM得分
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