为医疗机器学习预测模型的可信度评估提供12条核心准则
Consensus statement on the credibility assessment of ML predictors
- 基于专家共识提出12条评估框架，强调因果知识与误差量化
- 对比生物物理模型，指出隐式因果关系带来的可靠性挑战
- 适合研究者、开发者及监管机构参考，提升临床应用可信度
机器学习（ML）预测模型在计算机辅助医学中的快速集成，已革新了对难以直接测量的感兴趣量（QIs）的估计。然而，这些预测模型的可信度至关重要，尤其是在影响高风险医疗决策时。本文由计算机辅助医学实践社区的专家共同撰写，提出一份共识声明，构建了评估ML预测模型可信度的十二项关键原则，强调因果知识、严格误差量化及对偏差的鲁棒性。通过对比生物物理模型，揭示了隐式因果知识带来的独特挑战，并提出确保可靠性和适用性的策略。本建议旨在指导研究人员、开发者和监管机构对临床与生物医学场景中的ML预测模型进行严谨评估与部署。
原文摘要 · Abstract (English)
The rapid integration of machine learning (ML) predictors into in silico medicine has revolutionized the estimation of quantities of interest (QIs) that are otherwise challenging to measure directly. However, the credibility of these predictors is critical, especially when they inform high-stakes healthcare decisions. This position paper presents a consensus statement developed by experts within the In Silico World Community of Practice. We outline twelve key statements forming the theoretical foundation for evaluating the credibility of ML predictors, emphasizing the necessity of causal knowledge, rigorous error quantification, and robustness to biases. By comparing ML predictors with biophysical models, we highlight unique challenges associated with implicit causal knowledge and propose strategies to ensure reliability and applicability. Our recommendations aim to guide researchers, developers, and regulators in the rigorous assessment and deployment of ML predictors in clinical and biomedical contexts.
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