一个可解释的模糊框架来评估孕前分类
Matías Salinas1,2,3,4,5, Daira Velandia6, Leondry Mayeta-Revilla1,2,3,4
1PhD Program in Health Sciences and Engineering, Universidad de Valparaíso, Valparaíso 2540064, Chile.
Biomedicines
|June 26, 2025
概括
一个新的可解释的框架,SK-MOEFS,使用模糊逻辑和进化优化准确预测子宫前风险. 它提供了可解释的规则,以便在产妇护理中更好地做出临床决策.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 孕产妇健康 孕产妇健康
背景情况:
- 孕前是全球主要的孕产妇发病原因之一.
- 准确和可解释的预测系统对于临床决策至关重要.
- 现有的模型往往缺乏透明度,阻碍了临床采用.
研究的目的:
- 引入SK-MOEFS,为产前风险分类提供一个可解释的框架.
- 开发一个产生临床解释规则的系统.
- 通过透明的人工智能增强产妇护理的决策.
主要方法:
- 利用模糊的决策树与规则优化遗传算法集成.
- 采用多目标进化优化,以提高准确性和可解释性.
- 通过使用开源工具,在574个第三季度怀孕的多民族队列中接受培训和验证.
主要成果:
- 获得了91%的分类准确度,0.89的AUC和0.88的回忆.
- 在预测准确性和透明度方面表现优于标准可解释模型.
- 展示了专注于最小化错误负面的关键风险分层的重点.
结论:
- SK-MOEFS提供了一种透明和可靠的方法来预测妊娠前风险.
- 该框架的规则翻译和自然语言输出增强了临床实用性.
- 提供了人工智能和临床判断之间的桥梁,以改善孕产妇护理结果.
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