From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction

从连续预测变量到临床阈值：基于指南的分类法在缺血性卒中预后预测中性能权衡的初步证据

Abstract: Machine learning models achieve strong predictive accuracy for 90-day outcome prediction in acute ischaemic stroke, yet clinical adoption is limited by the misalignment of model explanations with clinicians’ reasoning. 摘要： 机器学习模型在急性缺血性卒中 90 天预后预测方面表现出强大的预测准确性，但由于模型解释与临床医生的推理逻辑不匹配，其临床应用受到限制。

Motivated by a clinician user study calling for clinical guideline-aligned cut-offs, we ask whether continuous predictors can be replaced by clinically informed categorical encodings without sacrificing performance. 受一项呼吁采用符合临床指南界限的临床医生用户研究的启发，我们探讨了在不牺牲性能的前提下，是否可以用基于临床知识的分类编码来替代连续预测变量。

On a multi-centre European registry stratified into three treatment cohorts, we compare standard and fully categorised gradient-boosted models, the latter using stroke guideline-aligned, treatment-specific thresholds. 基于一个分为三个治疗队列的多中心欧洲登记数据库，我们比较了标准梯度提升模型与完全分类梯度提升模型，后者使用了符合卒中指南的、针对特定治疗的阈值。

The fully categorised models are statistically indistinguishable from their continuous counterparts in two of the treatment cohorts, with a significant drop in predictive accuracy in one cohort. 研究发现，在三个治疗队列中的两个，完全分类模型与连续变量模型在统计学上没有显著差异，仅在一个队列中预测准确性出现了显著下降。

Global feature importance rankings remain consistent, suggesting that discretising continuous predictors into guideline-based categories preserves the core hierarchy of prognostic factors across all treatment groups. 全局特征重要性排名保持一致，这表明将连续预测变量离散化为基于指南的类别，能够保留所有治疗组中预后因素的核心层级结构。

Guideline-based categorisation is thus a viable design choice for stroke-outcome models. 因此，基于指南的分类法是卒中预后模型的一种可行设计选择。