用玻璃盒人工智能和土著智慧和生活经验编码设计土著围产期心理健康的临床预测模型
Guanjin Wang1,2, Hachem Bennamoun1, Wai Hang Kwok3
1School of Information Technology, Murdoch University, Perth, Australia.
Studies in health technology and informatics
|September 25, 2024
概括
对土著母亲的围产期心理健康支持至关重要. 一个基于实践经验的新人工智能工具提供了对母体风险和保护因素的文化敏感识别,以获得更好的护理.
科学领域:
- 土著健康土著健康
- 围产期心理健康 围产期心理健康
- 医疗保健中的人工智能
背景情况:
- 围产期心理健康对于母亲和婴儿的福祉至关重要.
- 原住民母亲经历了与殖民和创伤有关的重大心理健康差异.
- 现有的选工具往往缺乏文化适应性.
研究的目的:
- 开发一种文化敏感的人工智能工具,用于评估土著母亲的围产期心理健康.
- 利用原住民生活经验,识别关键的孕产妇风险和保护因素.
- 支持医疗保健专业人员提供整体,文化安全的护理.
主要方法:
- 使用一个可解释的增强机器 (EBM) 模型.
- 用反映原住民生活经验的数据训练人工智能模型.
- 开发一种可解释的人工智能解决方案,用于风险和保护因素的识别.
主要成果:
- 人工智能模型在试点测试中显示出了前景.
- 确定了与土著母亲心理健康相关的特定保护和风险因素.
- 提供可解释的预测,以指导临床决策.
结论:
- 基于人工智能的方法提供了一种文化敏感的方法,用于围产期心理健康评估.
- 这种工具可以加强为土著母亲和家庭提供量身定制和整体的护理.
- 将人工智能与土著知识相结合,可以解决健康差异.
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