调查原住民围产期心理健康的保护性和风险因素以及预测性见解:可解释的人工智能方法
Guanjin Wang1,2, Hachem Bennamoun1, Wai Hang Kwok3
1School of Information Technology, Murdoch University, Perth, Australia.
Journal of medical Internet research
|April 30, 2025
概括
可解释性AI (XAI) 模型可以通过分析文化优势和风险因素来预测土著母亲的心理痛苦. 这些工具支持医疗保健提供者为围产期心理健康查做出公正的决定.
科学领域:
- 医疗保健中的人工智能
- 围产期心理健康问题
- 土著健康研究土著健康研究
背景情况:
- 围产期抑郁和焦虑对母亲和婴儿的福祉构成重大风险,土著妇女由于殖民的影响而面临更高的脆弱性.
- 婴儿准备好 (BCYR) 模型提供了基于优势的数字化方法,但依赖传统风险得分可能会忽视原住民母亲的背景因素.
- 需要改进临床决策工具,以更好地支持土著母亲的产前心理健康,考虑到文化优势和保护因素.
研究的目的:
- 探索可解释的人工智能 (XAI) 驱动的机器学习,以文化为基础,基于强项的预测土著母亲的围产期心理痛苦.
- 开发能够识别和评估影响风险和保护因素的模型.
- 确保人工智能驱动的决策对医疗保健专业人员来说是透明和可解释的.
主要方法:
- 利用了BCYR计划 (珀斯,华盛顿州,2021-2023) 中293名土著母亲的非身份化数据.
- 从数据集中选择了20个关键变量,包括文化优势,生活事件和心理社会因素,使用Kessler-5来测量痛苦.
- 开发和比较机器学习模型 (EBM,RF,XGBoost等) ),使用XAI技术 (SHAP,LIME) 进行解释.
主要成果:
- 可解释增强机器 (EBM) 显示出优异的预测性能 (精度为0.849,AUC为0.821).
- XAI技术始终确定了诸如"感到孤独"",责怪自己"",让家人感到自豪"等关键因素.
- 提供了个人层面的见解,可视区分保护因素和风险因素对预测的影响.
结论:
- 基于XAI的模型显示了预测土著母亲心理痛苦的巨大潜力.
- 这些模型提供了对影响因素的清晰,人类可解释的解释,有助于临床决策.
- 这些发现表明,XAI可以为原住民妇女提供更知情,更公平的围产期心理健康查.
关键词:
人工智能辅助的决策这是土著的原住民.原住民母亲的母亲.澳大利亚 澳大利亚 澳大利亚焦虑是一种焦虑.人工智能的人工智能是人工智能.童年经历 童年经历 童年经历文化优势 文化优势决策支持提供了决策支持.在决策过程中做出决定.抑郁 抑郁症 抑郁症 抑郁症 是一种家庭暴力 家庭暴力可以解释的人工智能AI婴儿婴儿婴儿婴儿婴儿婴儿婴儿婴儿健康 婴儿健康生活事件 生活事件机器学习是机器学习.母亲的母亲的母亲.孕产妇健康 孕产妇健康心理健康 心理健康母亲 母亲 母亲 母亲周产阶段 周产阶段围产期心理健康 围产期心理健康预测性 预测性 预测性保护因素 保护因素心理上的痛苦 心理上的痛苦心理风险 心理风险关系关系关系关系关系关系.有关风险因素的风险因素.使用物质使用物质.一个女人,一个女人,一个女人.担心 担心 担心更多相关视频
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