Translation of Black-Box Clinical Prediction Models into Standalone Transparent Nomograms: Temporal External Validation in Heart Transplantation

2026-09-07

AI Summary

本文通过PRiSM方法将黑盒临床预测模型转换为透明的独立列线图，以提高可审计性，并在50,356名心脏移植受者中验证了其有效性。

Abstract

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.

Problem

• black-box clinical prediction models
• standalone nomograms
• external validation
• heart transplantation

Innovation

• PRiSM
• black-box clinical prediction models
• transparent nomograms
• temporal external validation
• noninferiority criterion