弥合信任鸿沟：经临床验证的混合可解释人工智能用于孟加拉国产科健康风险评估
Bridging the Trust Gap: Clinician-Validated Hybrid Explainable AI for Maternal Health Risk Assessment in Bangladesh
- Independent Researcher, Boston MA 02116, USA（独立研究者）
- Independent Researcher, Dhaka, Bangladesh（独立研究者）
- Medical Officer, Sonargaon Sheba General Hospital, Narayanganj, Bangladesh（医疗官）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出混合可解释人工智能框架，结合模糊逻辑与SHAP解释，提升产科健康风险评估的可解释性和临床信任度。
AI中文摘要：
尽管机器学习在产科健康风险预测中展现出潜力，但在资源有限的环境中临床应用面临关键障碍：缺乏可解释性和信任。本研究提出了一种混合可解释人工智能（XAI）框架，结合事前模糊逻辑与事后SHAP解释，通过系统性的临床反馈进行验证。我们基于1014份产科健康记录开发了模糊-XGBoost模型，实现了88.67%的准确率（ROC-AUC:0.9703）。在孟加拉国14名医疗专业人员的验证研究中，混合解释方法在三个临床案例中受到71.4%的青睐，54.8%的受访者表示对临床应用有信任。SHAP分析发现医疗保健获取是主要预测因素，而设计的模糊风险评分排名第三，验证了临床知识的整合（r=0.298）。临床医生重视整合的临床参数，但指出了关键缺口：产科史、孕周和连接障碍。本研究证明，将可解释的模糊规则与特征重要性解释相结合，可以提高实用性和信任，为XAI在产科医疗中的部署提供实用见解。
英文摘要：
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.
