通过被动感应改善抑郁症严重性预测:症状分析方法
Sabinakhon Akbarova1, Myeongji Im1, Suhyun Kim1
1Research and Development Department, Huno, Seoul 04146, Republic of Korea.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
数字表型化使用移动设备数据来检测抑郁症的严重程度. 一种新的症状分析方法提高了预测准确度,达到F1得分高达0.86.
科学领域:
- 数字健康数字健康
- 心理健康信息学心理健康信息学
- 计算精神病学是一种计算精神病学.
背景情况:
- 抑郁症的诊断依赖于主观的方法,如采访和调查.
- 与传统方法相比,持续监测抑郁症是一个挑战.
- 数字表型化为心理健康评估提供被动,客观的数据收集.
研究的目的:
- 开发和评估一种新的数字表型化方法来检测抑郁症的严重程度.
- 用被动传感器数据提高预测抑郁症严重程度的准确性.
- 引入和验证抑郁症评估的"症状分析"方法.
主要方法:
- 在三个月内从381名参与者收集了被动传感器数据和患者健康问卷 (PHQ-9) 评分.
- 开发了一个"症状概况向量",代表了九种抑郁症状,它们的概率和意义.
- 使用传感器特征与症状概况向量来预测抑郁症严重程度 (没有/轻度,中度,严重) 的机器学习模型性能比较.
主要成果:
- 症状分析方法显著提高了抑郁症严重程度预测的准确性.
- F1分数增加了0.09,平均改善了0.05.
- 抑郁症严重程度预测的最高F1得分为0.86.
结论:
- 症状分析提高了用于抑郁症严重程度检测的数字表型的准确性.
- 被动感应与症状分析相结合,为持续的心理健康监测提供了一个有前途的工具.
- 这种方法可以帮助区分抑郁模式并提高诊断精度.
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