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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 tomore accurate predictions, but are non-interpretable. This study shows it ispossible to estimate hospitalization for congestive heart failure by a 30seconds single-lead electrocardiogram signal. Using a machine learning approachnot only results in greater predictive power but also provides clinicallymeaningful interpretations. We train an eXtreme Gradient Boosting acceleratedfailure time model and exploit SHapley Additive exPlanations values to explainthe effect of each feature on predictions. Our model achieved a concordanceindex of 0.828 and an area under the curve of 0.853 at one year and 0.858 attwo years on a held-out test set of 6,573 patients. These results show that arapid test based on an electrocardiogram could be crucial in targeting andtreating high-risk individuals.

Interpretable estimation of the risk of heart failure hospitalization from a 30-second electrocardiogram