论文标题
通过干预预测提高临床风险评分的可解释性
Boosting the interpretability of clinical risk scores with intervention predictions
论文作者
论文摘要
机器学习系统对通过风险分数预测患者不良事件的预测显示出巨大的希望。但是，根据培训数据中存在的干预政策，这些风险分数隐含地编码了有关患者可能会接受的未来干预措施的假设。没有这种重要的环境，这些系统的预测对于临床医生而言是不太可解释的。我们提出了一个干预政策和不利事件风险的联合模型，以此作为明确传达模型对未来干预措施的假设的一种手段。我们开发了一种关于Mimic-III的干预政策模型，这是一种现实世界中的ICU数据集，并讨论了一些用例突出该方法的实用性。我们展示了典型的风险评分（例如死亡率的可能性）与未来的干预概率分数如何相结合，从而导致更可解释的临床预测。
Machine learning systems show significant promise for forecasting patient adverse events via risk scores. However, these risk scores implicitly encode assumptions about future interventions that the patient is likely to receive, based on the intervention policy present in the training data. Without this important context, predictions from such systems are less interpretable for clinicians. We propose a joint model of intervention policy and adverse event risk as a means to explicitly communicate the model's assumptions about future interventions. We develop such an intervention policy model on MIMIC-III, a real world de-identified ICU dataset, and discuss some use cases that highlight the utility of this approach. We show how combining typical risk scores, such as the likelihood of mortality, with future intervention probability scores leads to more interpretable clinical predictions.
