从自然语言中间接识别围产期心理社会风险
Kristen C Allen1, Alex Davis1, Tamar Krishnamurti2
1Carnegie Mellon University.
IEEE transactions on affective computing
|June 2, 2023
概括
这项研究表明,通过简短的日记记录来分析语言,可以帮助识别围产期的心理健康风险,如抑郁症. 这种间接方法有望发现人们可能不会直接披露的敏感问题.
科学领域:
- 心理社会健康 心理健康
- 围产期心理健康 围产期心理健康
- 在医疗保健中的自然语言处理.
背景情况:
- 围产期心理社会风险,如抑郁症和亲密伴侣暴力,会对父母和孩子产生负面影响.
- 污名化阻止了这些风险的直接披露,阻碍了及时的医疗保健干预.
- 识别有风险的个体对于有效的围产期心理健康护理至关重要.
研究的目的:
- 探索使用简短的日记条目来间接识别围产期心理社会风险.
- 检查日记记录中的语言模式,以预测抑郁症和亲密伴侣暴力.
- 评估基于文本的特征在检测污名化的健康风险方面的有效性.
主要方法:
- 使用主题建模来从日记条目中提取主题.
- 采用了词典信息的情感分析来确定情感视角.
- 应用调节回归来预测抑郁和心理攻击的查措施.
主要成果:
- 日记文本功能,包括话题模型和情绪分析,在预测抑郁症方面显示出显著的前景.
- 基于文本的抑郁症预测与传统的闭式查问题的结果非常相匹配.
- 虽然对亲密伴侣暴力的预测效果较低,但主题模型识别了与已知的风险因素相关的主题.
结论:
- 对非正式文本的语言分析,如日记条目,为评估围产期心理社会风险提供了一种可行的间接方法.
- 这种方法可以帮助医疗保健专业人员发现个人可能不愿透露的敏感问题.
- 对基于文本的特征进行进一步的研究可以提高围产期心理健康挑战的早期识别和管理.
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