Rapid Anxiety and Depression Diagnosis in Young Children Enabled by Wearable Sensors and Machine Learning.
Rapid Anxiety and Depression Diagnosis in Young Children Enabled by Wearable Sensors and Machine Learning.
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通过可穿戴传感器和机器学习实现幼儿焦虑和抑郁的快速诊断。
DOI：
10.1109/embc.2018.8513327
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Muzik,Maria
中科院分区：
文献类型：
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作者：
McGinnis,RyanS;McGinnis,EllenW;Hruschak,Jessica;Lopez-Duran,NestorL;Fitzgerald,Kate;Rosenblum,KatherineL;Muzik,Maria
本文提出了一种诊断幼儿焦虑和抑郁的新方法。目前，诊断需要数小时的结构化临床访谈和标准化问卷调查，时间跨度为数天或数周。我们建议使用一个90秒的恐惧感应任务，在此期间，参与者的运动监测使用市售的可穿戴传感器。机器学习和从临床上最可行的20秒任务阶段提取的数据用于预测有和没有内化诊断的儿童样本的诊断。我们研究了各种特征集和建模方法的性能，以确定性能最好的逻辑回归，其诊断准确率为80%。这种准确性与现有的诊断技术相当，但所需的时间和成本仅为目前所需的一小部分。这些结果指向未来使用这种方法在临床环境中诊断儿童内化障碍。
This paper presents a new approach for diagnosing anxiety and depression in young children. Currently, diagnosis requires hours of structured clinical interviews and standardized questionnaires spread over days or weeks. We propose the use of a 90-second fear induction task during which time participant motion is monitoring using a commercially available wearable sensor. Machine learning and data extracted from the most clinically feasible 20-second phase of the task are used to predict diagnosis in a sample of children with and without an internalizing diagnosis. We examine the performance of a variety of feature sets and modeling approaches to identify the best performing logistic regression that provides a diagnostic accuracy of 80%. This accuracy is comparable to existing diagnostic techniques, but at a small fraction of the time and cost currently required. These results point toward the future use of this approach in a clinical setting for diagnosing children with internalizing disorders.