将连续预测变量转为临床指南阈值，性能损失可控，更易被医生接受。
From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction
- 用临床指南定义的分类阈值替代连续变量，保持模型结构不变。
- 在两个治疗组中性能与原模型无显著差异，一个组略有下降。
- 关键因素重要性排序一致，适合临床可解释性需求的模型设计。
机器学习模型在急性缺血性卒中90天预后预测中表现优异，但因解释方式与临床思维不一致，难以推广应用。基于临床用户调研提出的对指南对齐分割点的需求，本文研究将连续预测变量替换为基于临床指南、治疗特异的分类编码是否会影响性能。在分属三个治疗组的多中心欧洲注册队列上，比较了标准与完全分类的梯度提升模型。结果显示，在两个治疗组中，分类模型与连续模型性能无统计学差异，仅在一个组出现显著性能下降。全局特征重要性排序保持一致，表明基于指南的分类能保留各治疗组的核心预后因素层级。因此，基于指南的分类是卒中预后模型的可行设计选择。
原文摘要 · Abstract (English)
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. 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. 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. 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. 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. Guideline-based categorisation is thus a viable design choice for stroke-outcome models.
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