通过融合概率与规则模型，实现无创产前检测中高灵敏度与可解释性的统一。
Medical priority fusion: achieving dual optimization of sensitivity and interpretability in nipt anomaly detection
- 用加权融合将贝叶斯概率与决策树规则结合，受医学约束优化。
- 在1687个样本上达89.3%敏感性，且可解释性评分80%。
- 适合临床部署，解决医疗AI中准确率与透明度的矛盾。
临床机器学习在高风险医疗场景中面临核心困境：高性能算法常牺牲医生决策所需的可解释性，而可解释方法在复杂情况下又降低敏感性。这一矛盾在无创产前检测（NIPT）中尤为突出，因染色体异常漏诊后果严重，但监管要求可解释AI。本文提出医疗优先融合（MPF）框架，通过数学严谨的加权融合，系统整合朴素贝叶斯概率推理与决策树规则逻辑，并施加明确医学约束。在1,687个真实世界NIPT样本上验证，样本类不平衡比为43.4:1，采用分层5折交叉验证，结合全面消融实验与麦内马尔配对检验。结果表明，MPF同时实现双重优化：敏感性达89.3%（95%置信区间：83.9–94.7%），可解释性评分为80%，显著优于单一算法（麦内马尔检验，p < 0.001）。最优融合配置达到临床部署等级A标准，效应量大（d = 1.24），首次实现诊断准确与决策透明兼具的可部署解决方案。本研究证明，受医学约束的算法融合可破解可解释性与性能的权衡，为高风险医疗决策支持系统提供数学框架。
原文摘要 · Abstract (English)
Clinical machine learning faces a critical dilemma in high-stakes medical applications: algorithms achieving optimal diagnostic performance typically sacrifice the interpretability essential for physician decision-making, while interpretable methods compromise sensitivity in complex scenarios. This paradox becomes particularly acute in non-invasive prenatal testing (NIPT), where missed chromosomal abnormalities carry profound clinical consequences yet regulatory frameworks mandate explainable AI systems. We introduce Medical Priority Fusion (MPF), a constrained multi-objective optimization framework that resolves this fundamental trade-off by systematically integrating Naive Bayes probabilistic reasoning with Decision Tree rule-based logic through mathematically-principled weighted fusion under explicit medical constraints. Rigorous validation on 1,687 real-world NIPT samples characterized by extreme class imbalance (43.4:1 normal-to-abnormal ratio) employed stratified 5-fold cross-validation with comprehensive ablation studies and statistical hypothesis testing using McNemar's paired comparisons. MPF achieved simultaneous optimization of dual objectives: 89.3% sensitivity (95% CI: 83.9-94.7%) with 80% interpretability score, significantly outperforming individual algorithms (McNemar's test, p < 0.001). The optimal fusion configuration achieved Grade A clinical deployment criteria with large effect size (d = 1.24), establishing the first clinically-deployable solution that maintains both diagnostic accuracy and decision transparency essential for prenatal care. This work demonstrates that medical-constrained algorithm fusion can resolve the interpretability-performance trade-off, providing a mathematical framework for developing high-stakes medical decision support systems that meet both clinical efficacy and explainability requirements.
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