论文标题
30秒心电图的心力衰竭住院风险的可解释估计
Interpretable estimation of the risk of heart failure hospitalization from a 30-second electrocardiogram
论文作者
论文摘要
医疗保健中的生存建模依赖于可解释的统计模型；然而，他们的基本假设通常很简单，因此是不现实的。机器学习模型可以估计更复杂的关系并导致更准确的预测，但无解剖。这项研究表明，可以通过30秒的单个铅心电图信号来估计充血性心力衰竭的住院。使用机器学习方法不仅会产生更大的预测能力，而且还提供了临床上有意义的解释。我们训练一个极端的梯度增强的加速故障时间模型，并利用Shapley添加说明值，以解释每个特征对预测的影响。我们的模型在一年时达到了0.828的一致性指数，曲线下的面积为0.853，在两年内，在6,573例患者的持有测试套装中达到了0.858。这些结果表明，基于心电图的快速测试对于靶向和治疗高风险个体至关重要。
Survival modeling in healthcare relies on explainable statistical models; yet, their underlying assumptions are often simplistic and, thus, unrealistic. Machine learning models can estimate more complex relationships and lead to more accurate predictions, but are non-interpretable. This study shows it is possible to estimate hospitalization for congestive heart failure by a 30 seconds single-lead electrocardiogram signal. Using a machine learning approach not only results in greater predictive power but also provides clinically meaningful interpretations. We train an eXtreme Gradient Boosting accelerated failure time model and exploit SHapley Additive exPlanations values to explain the effect of each feature on predictions. Our model achieved a concordance index of 0.828 and an area under the curve of 0.853 at one year and 0.858 at two years on a held-out test set of 6,573 patients. These results show that a rapid test based on an electrocardiogram could be crucial in targeting and treating high-risk individuals.
