论文标题
假设干预措施下的临床结果预测 - 反事实推理的表示框架
Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning
论文作者
论文摘要
大多数机器学习（ML）模型仅用于预测；为因果解释其预测或参数/属性没有任何选择。这可能会阻碍卫生系统在临床决策过程中采用ML模型的能力，在这种过程中，在假设研究（即反事实推理/解释）下预测结果的需求和渴望很高。在这项研究中，我们介绍了一个新的表示学习框架（即部分概念瓶颈），该框架将反事实解释作为风险模型的嵌入式属性。尽管结构变化以共同优化预测准确性和反事实推理，但我们方法的准确性与仅预测模型相当。我们的结果表明，我们提出的框架有可能帮助研究人员和临床医生改善个性化护理（例如，通过研究干预的假设差异效应）
Most machine learning (ML) models are developed for prediction only; offering no option for causal interpretation of their predictions or parameters/properties. This can hamper the health systems' ability to employ ML models in clinical decision-making processes, where the need and desire for predicting outcomes under hypothetical investigations (i.e., counterfactual reasoning/explanation) is high. In this research, we introduce a new representation learning framework (i.e., partial concept bottleneck), which considers the provision of counterfactual explanations as an embedded property of the risk model. Despite architectural changes necessary for jointly optimising for prediction accuracy and counterfactual reasoning, the accuracy of our approach is comparable to prediction-only models. Our results suggest that our proposed framework has the potential to help researchers and clinicians improve personalised care (e.g., by investigating the hypothetical differential effects of interventions)
