Death After Liver Transplantation: Mining Interpretable Risk Factors for Survival Prediction

This study introduces a novel approach to mine risk factors for short-term death after liver transplantation (LT). The method outputs intelligible survival models by combining Cox's regression with a genetic programming technique known as multi-objective symbolic regression (MOSR). We consider 485 Electronic Health Records (EHRs) of patients who underwent LT, containing information on hospitalization and preoperative conditions, with a focus on infections and colonizations by multi-resistant Gram-negative bacteria. We evaluate MOSR outcomes against several performance metrics and demonstrate that they are well-calibrated, predictive, safe, and parsimonious. Finally, we select the most promising post-LT early survival risk score based on information criteria, performance, and out-of-distribution safety. Validating this technique at a multicenter level could improve service pipeline logistics through a trustworthy machine-learning method.