对预防慢性疾病的多类反事实解释的估计和合规性评估
IEEE journal of biomedical and health informatics
|November 6, 2024
概括
可解释的AI有助于预测COPD患者的心血管疾病风险. 通过符合性验证的反事实解释,改善了个性化风险降低建议,以获得更好的医疗保健结果.
科学领域:
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
- 可解释的人工智能
背景情况:
- 医疗保健中的个性化指导需要可解释的AI解决方案.
- 临床决策支持工具必须与医学知识保持一致.
- 心血管疾病 (CVD) 风险评估对于慢性阻塞性肺病 (COPD) 患者至关重要.
研究的目的:
- 探索可解释的AI来描述COPD患者心血管疾病风险.
- 为了比较两个反事实解释方法: MUCH 和 DiCE.
- 为了引入和验证解释性评估的反事实性合规性.
主要方法:
- 通过使用弗雷明汉风险评分将9613名COPD患者记录分为低,中等和高心血管疾病风险类别.
- 通过哈尔顿抽样 (MUCH) 和多元反事实解释 (DiCE) 使用MULti反事实解释生成反事实解释.
- 实施了错误控制机制,并引入了用于验证的反事实合规性.
主要成果:
- 许多解释通常比DiCE更合理和更容易区分.
- DiCE的解释表明了更好的可用性,近距离和稀疏性.
- 过不合规的解释提高了解释的整体质量.
结论:
- 将反事实解释与符合性评估相结合,可以提高AI在医疗保健中的解释性.
- 这种方法支持开发个性化风险降低建议.
- 对于临床决策支持工具,需要进一步的验证和专家评估.
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