Unravelling Heterogeneity: A Hybrid Machine Learning Approach to Predict Post-discharge Complications in Cardiothoracic Surgery

Predicting post-discharge complications in cardiothoracic surgery is of utmost importance to improve clinical outcomes. Machine Learning (ML) techniques have been successfully applied in similar tasks, aiming at short time windows and in specific surgical conditions. However, as the target horizon is extended and the impact of unpredictable external factors rises, the complexity of the task increases, and traditional predictive models struggle to reproduce good performances. This study presents a two-step hybrid learning methodology to address this problem. Building up from identifying unique sub-groups of patients with shared characteristics, we then train individual supervised classification models for each sub-group, aiming at improved prediction accuracy and a more granular understanding of each decision. Our results show that specific sub-groups demonstrate substantially better performance when compared to the baseline model without sub-divisions, while others do not benefit from specialised models. Strategies such as the one presented may catalyse the success of applied ML solutions by contributing to a better understanding of their behaviour in different regions of the data space, leading to an informed decision-making process.