Detecting Bipolar Depression From Geographic Location Data.
Detecting Bipolar Depression From Geographic Location Data.
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DOI：
10.1109/tbme.2016.2611862
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发表时间：
2017-08
期刊：
影响因子：
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通讯作者：
De Vos M
中科院分区：
文献类型：
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作者：
Palmius N;Tsanas A;Saunders KEA;Bilderbeck AC;Geddes JR;Goodwin GM;De Vos M
本文旨在通过对双相情感障碍（BD）患者的前瞻性社区研究，使用移动的手机记录的地理定位运动来识别抑郁症的发作期。在3个月内收集了来自22名BD参与者和14名健康对照（HC）的分析地理位置记录。参与者使用每周问卷（QIDS-SR 16）报告他们的抑郁症。通过检测和去除不精确的数据点对记录的位置数据进行预处理，并提取特征以评估参与者地理运动的水平和规律性。使用包装器特征选择方法选择特征的子集，并呈现给1）线性回归模型和具有用于问卷分数估计的逻辑链接函数的二次广义线性模型;和2）基于BD参与者的问卷响应的用于抑郁检测的二次判别分析分类器。HC参与者没有报告抑郁症状，他们的特征显示出与非抑郁BD参与者相似的分布。使用BD参与者的地理位置衍生特征进行问卷评分估计的最佳平均绝对误差率为3.73，而抑郁症检测的最佳平均绝对误差率为3.73。（中位数± IQR）F1评分为0.857 ± 0.022，使用5个特征（分类准确度：0.849 ± 0.016;敏感度：0.839 ± 0.014;特异度：0.872 ± 0.047）。这些结果表明，地理运动和双相情感障碍的抑郁症之间有很强的联系。据我们所知，这是第一个被动记录的双相情感障碍抑郁客观标志物的社区研究。这些技术可以帮助个人监测他们的抑郁症，并使医疗保健提供者能够检测出那些需要护理或治疗的人。
This paper aims to identify periods of depression using geolocation movements recorded from mobile phones in a prospective community study of individuals with bipolar disorder (BD). Anonymized geographic location recordings from 22 BD participants and 14 healthy controls (HC) were collected over 3 months. Participants reported their depressive symptomatology using a weekly questionnaire (QIDS-SR16). Recorded location data were preprocessed by detecting and removing imprecise data points and features were extracted to assess the level and regularity of geographic movements of the participant. A subset of features were selected using a wrapper feature selection method and presented to 1) a linear regression model and a quadratic generalized linear model with a logistic link function for questionnaire score estimation; and 2) a quadratic discriminant analysis classifier for depression detection in BD participants based on their questionnaire responses. HC participants did not report depressive symptoms and their features showed similar distributions to nondepressed BD participants. Questionnaire score estimation using geolocation-derived features from BD participants demonstrated an optimal mean absolute error rate of 3.73, while depression detection demonstrated an optimal (median ± IQR) F1 score of 0.857 ± 0.022 using five features (classification accuracy: 0.849 ± 0.016; sensitivity: 0.839 ± 0.014; specificity: 0.872 ± 0.047). These results demonstrate a strong link between geographic movements and depression in bipolar disorder. To our knowledge, this is the first community study of passively recorded objective markers of depression in bipolar disorder of this scale. The techniques could help individuals monitor their depression and enable healthcare providers to detect those in need of care or treatment.