Interpretable machine learning-based predictive modeling of patient outcomes following cardiac surgery.

ABSTRACT:

Background

The clinical applicability of machine learning predictions of patient outcomes following cardiac surgery remains unclear. We applied machine learning to predict patient outcomes associated with high morbidity and mortality after cardiac surgery and identified the importance of variables to the derived model's performance.

Methods

We applied machine learning to the Society of Thoracic Surgeons Adult Cardiac Surgery Database to predict postoperative hemorrhage requiring reoperation, venous thromboembolism (VTE), and stroke. We used permutation feature importance to identify variables important to model performance and a misclassification analysis to study the limitations of the model.

Results

The study dataset included 662,772 subjects who underwent cardiac s

SUBMITTER: Abbasi A 

PROVIDER: S-EPMC11133766 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature