在怀孕期间使用自我报告数据进行抑郁症的数字表型化
Kristen Allen1, Samantha Rodriguez2, Laila Hayani3
1Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, United States of America; Allegheny County Department of Human Services, Pittsburgh, PA, United States of America.
Journal of affective disorders
|August 13, 2024
概括
移动健康应用程序可以帮助怀孕期间识别抑郁症症状. 每天的情绪和怀孕症状,甚至是简单的语言输入,可以改善早期发现和理解母亲的抑郁症.
科学领域:
- 围产期心理健康问题
- 数字健康干预措施 数字健康干预措施
- 计算语言学计算语言学
背景情况:
- 抑郁症是怀孕的常见并发症,往往未被发现并未得到充分治疗.
- 移动健康 (mHealth) 工具有可能在孕妇中早期识别抑郁症症状.
研究的目的:
- 调查从产前移动健康应用程序中患者报告的数据对预测怀孕期间抑郁风险的有用性.
- 评估各种数据类型的个人和综合预测价值,包括情绪,症状和书面语言.
主要方法:
- 一项观察性队列研究,涉及2062个怀孕.
- 利用产前支持应用程序收集患者病史,情绪,怀孕症状和书面语言的自我报告数据.
- 采用选择性推断和拉索建模来分析数据并预测30天和60天期间的抑郁风险.
主要成果:
- 抑郁症预测模型表现出不同的有效性 (AUC 0.64-0.83).
- 结合个人病史,日常情绪和急性怀孕症状的模型是最有预测力的.
- 日常情绪成为后续抑郁症状的最强预测因素;自然语言输入增强了准确性和上下文理解.
结论:
- 通过数字工具,简单的,患者报告的数据,包括自然语言,可以帮助早期识别抑郁症症状.
- 提供了对母亲抑郁症生活背景的更细致的理解.
- 调查结果仅限于使用该应用程序的数字素养,自我激励的孕妇.
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