一种用于生成临床风险评分的新型可解释机器学习系统：在回顾性队列研究中预测早期死亡或非计划再入院的应用
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study
AI总结：
本文提出基于ShapleyVIC的可解释变量选择方法，结合AutoScore自动生成临床风险评分，在预测早期死亡或非计划再入院研究中仅用6个变量即达到与16变量模型相当的性能。
摘要：
风险评分广泛用于临床决策，通常由逻辑回归模型生成。基于机器学习的方法可能有助于识别重要预测因子，但这种“黑箱”变量选择限制了可解释性，并且从单一模型评估的变量重要性可能存在偏差。我们提出一种稳健且可解释的变量选择方法，使用最近开发的Shapley变量重要性云（ShapleyVIC），该方法考虑了跨模型的变异性。我们的方法评估并可视化整体变量贡献，以进行深入推断和透明变量选择，并过滤掉非显著贡献者以简化模型构建步骤。我们从变量贡献中推导出集成变量排序，该排序易于与自动化、模块化的风险评分生成器AutoScore集成，便于实现。在一项关于早期死亡或非计划再入院的研究中，ShapleyVIC从41个候选变量中选择了6个变量来创建一个性能良好的模型，其性能与基于机器学习排序的16变量模型相似。
英文摘要：
Risk scores are widely used for clinical decision making and commonly generated from logistic regression models. Machine-learning-based methods may work well for identifying important predictors, but such 'black box' variable selection limits interpretability, and variable importance evaluated from a single model can be biased. We propose a robust and interpretable variable selection approach using the recently developed Shapley variable importance cloud (ShapleyVIC) that accounts for variability across models. Our approach evaluates and visualizes overall variable contributions for in-depth inference and transparent variable selection, and filters out non-significant contributors to simplify model building steps. We derive an ensemble variable ranking from variable contributions, which is easily integrated with an automated and modularized risk score generator, AutoScore, for convenient implementation. In a study of early death or unplanned readmission, ShapleyVIC selected 6 of 41 candidate variables to create a well-performing model, which had similar performance to a 16-variable model from machine-learning-based ranking.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
