将黑箱临床模型转为可逐项审查的透明预测图谱，适用于心脏移植患者。
Translation of Black-Box Clinical Prediction Models into Standalone Transparent Nomograms: Temporal External Validation in Heart Transplantation
- 基于源模型的效应形状与交互关系生成可解释预测图谱
- 在5万余例移植患者中验证，性能不劣于新模型且更简洁
- 开源工具支持医疗决策透明化，适合临床医生使用
我们将用于表格数据的黑箱临床预测模型转换为可逐项审计的独立预测图谱。PRiSM（结构化模型中的部分响应）保留了源模型中每个效应和交互的形态，而非仅关注哪些变量重要，并让结果自主选择和加权。我们在50,356名心脏移植受者中进行了测试，验证时间晚于训练时间。所有5个源模型——公开临床风险评分、逻辑回归、神经网络、随机森林和极端梯度提升——生成的图谱均满足预设非劣效性标准，且在判别能力、校准性和临床净获益方面表现良好。其中3个机器学习模型生成的图谱在判别能力上无显著差异，优于新提出的广义加性模型与可解释增强模型，且术语更少。PRiSM已作为开源Python包发布。
原文摘要 · Abstract (English)
We convert black-box clinical prediction models for tabular data into standalone nomograms that can be audited term by term. PRiSM (Partial Responses in Structured Models) takes the shape of each effect and interaction from the source model, not merely which variables mattered, and lets the outcome select and weight them. We tested this in 50,356 heart transplant recipients, with validation in a later era than training. Nomograms from all 5 source models - a public clinical risk score, logistic regression, neural networks, random forests and extreme gradient boosting - met a prespecified noninferiority criterion for discrimination before any further simplification, and generally preserved calibration and clinical net benefit. Those from the 3 machine-learning models showed no detectable difference in discrimination from de novo generalized additive and explainable boosting models, exceeded neural additive models, and carried fewer terms than the explainable boosting model. PRiSM is released as an open-source Python package.
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