Towards symbolic regression for interpretable clinical decision scores

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, owing 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 nonlinear constant optimization, allowing for seamless integration of rule-based logic into SR 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 with decision trees (DTs), random forests (RFs) and other SR methods, Brush achieves comparable or superior predictive performance while producing simpler models. This article is part of the discussion meeting issue 'Symbolic regression in the physical sciences'.