Predicting survival time for critically ill patients with heart failure using conformalized survival analysis

Heart failure (HF) is a critical public health issue, particularly for critically ill patients in intensive care units (ICUs). Predicting survival outcome in critically ill patients is a difficult yet crucially important task for timely treatment. This study utilizes a novel approach, conformalized survival analysis (CSA), designed to construct lower bounds on the survival time in critically ill HF patients with high confidence. Utilizing data from the MIMIC-IV dataset, this work demonstrates that CSA outperforms traditional survival models, such as the Cox proportional hazards model and Accelerated Failure Time (AFT) model, particularly in providing reliable, interpretable, and individualized predictions. By applying CSA to a large, real-world dataset, the study highlights its potential to improve decision-making in critical care, offering a more nuanced and accurate tool for prognostication in a setting where precise predictions and guaranteed uncertainty quantification can significantly influence patient outcomes.