用可解释的符号回归重构临床评分系统，让算法更透明可信。
Towards symbolic regression for interpretable clinical decision scores
- 结合决策树分割与非线性常数优化，支持规则逻辑融入符号回归
- 在SRBench上达到帕累托最优，复现两个临床评分系统准确率超90%
- 模型比决策树、随机森林更简单，适合医疗领域需要可解释性的场景
医疗决策常依赖风险方程与规则结合的算法，提供清晰标准化的治疗路径。传统符号回归（SR）仅限于连续函数形式及其参数搜索，难以建模此类决策逻辑。但因其生成数据驱动且可解释模型的能力，SR在构建临床风险评分方面具有潜力。为此，我们提出Brush算法，融合类决策树的分割策略与非线性常数优化，实现规则逻辑在符号回归和分类模型中的无缝集成。Brush在SRBench基准上达到帕累托最优性能，并成功复现两个广泛应用的临床评分系统，在保持高准确率的同时生成高度可解释模型。相比决策树、随机森林及其他符号回归方法，Brush在预测性能相当或更优的前提下，产生更简洁的模型。
原文摘要 · Abstract (English)
Medical decision-making makes frequent use of algorithms that combine risk equations with rules, providing clear and standardized treatment pathways. Symbolic regression (SR) traditionally limits its search space to continuous function forms and their parameters, making it difficult to model this decision-making. However, due to its ability to derive data-driven, interpretable models, SR holds promise for developing data-driven clinical risk scores. To that end we introduce Brush, an SR algorithm that combines decision-tree-like splitting algorithms with non-linear constant optimization, allowing for seamless integration of rule-based logic into symbolic regression and classification models. Brush achieves Pareto-optimal performance on SRBench, and was applied to recapitulate two widely used clinical scoring systems, achieving high accuracy and interpretable models. Compared to decision trees, random forests, and other SR methods, Brush achieves comparable or superior predictive performance while producing simpler models.
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