Explainable machine-learning predictions for complications after pediatric congenital heart surgery.

ABSTRACT: The quality of treatment and prognosis after pediatric congenital heart surgery remains unsatisfactory. A reliable prediction model for postoperative complications of congenital heart surgery patients is essential to enable prompt initiation of therapy and improve the quality of prognosis. Here, we develop an interpretable machine-learning-based model that integrates patient demographics, surgery-specific features and intraoperative blood pressure data for accurately predicting complications after pediatric congenital heart surgery. We used blood pressure variability and the k-means algorithm combined with a smoothed formulation of dynamic time wrapping to extract features from time-series data. In addition, SHAP framework was used to provide explanations of the prediction. Our model achie

SUBMITTER: Zeng X 

PROVIDER: S-EPMC8390484 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature