用可解释的AI帮医生判断孕妇风险，让临床信任度提升。
Bridging the Trust Gap: Clinician-Validated Hybrid Explainable AI for Maternal Health Risk Assessment in Bangladesh
- 融合模糊逻辑与SHAP分析，双重解释模型决策过程。
- 准确率达88.67%，ROC-AUC达0.9703，临床偏好率71.4%。
- 适合资源有限地区医疗AI落地，尤其关注医生反馈需求。
尽管机器学习在孕产妇健康风险预测中展现潜力，但在资源匮乏地区临床应用仍面临可解释性与信任缺失的障碍。本研究提出一种结合事前模糊逻辑与事后SHAP解释的混合可解释AI（XAI）框架，并通过孟加拉国14名医护人员的系统反馈进行验证。基于1,014条孕产妇健康记录构建模糊-XGBoost模型，准确率达到88.67%（ROC-AUC：0.9703）。临床验证显示，在三个临床案例中71.4%的医生更偏好混合解释方式，54.8%表示愿意用于临床。SHAP分析识别出医疗可及性为首要预测因子，工程化模糊风险评分位列第三，与临床知识相关性达r=0.298。医生认可集成临床参数，但也指出产科史、孕周和网络连接障碍等关键缺失。结果表明，融合可解释规则与特征重要性分析能有效提升模型实用性与临床信任，为孕产妇健康AI部署提供可行路径。
原文摘要 · Abstract (English)
While machine learning shows promise for maternal health risk prediction, clinical adoption in resource-constrained settings faces a critical barrier: lack of explainability and trust. This study presents a hybrid explainable AI (XAI) framework combining ante-hoc fuzzy logic with post-hoc SHAP explanations, validated through systematic clinician feedback. We developed a fuzzy-XGBoost model on 1,014 maternal health records, achieving 88.67% accuracy (ROC-AUC: 0.9703). A validation study with 14 healthcare professionals in Bangladesh revealed strong preference for hybrid explanations (71.4% across three clinical cases) with 54.8% expressing trust for clinical use. SHAP analysis identified healthcare access as the primary predictor, with the engineered fuzzy risk score ranking third, validating clinical knowledge integration (r=0.298). Clinicians valued integrated clinical parameters but identified critical gaps: obstetric history, gestational age, and connectivity barriers. This work demonstrates that combining interpretable fuzzy rules with feature importance explanations enhances both utility and trust, providing practical insights for XAI deployment in maternal healthcare.
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