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

Heart failure (HF) is a significant public health challenge, especially among critically ill patients in intensive care units (ICUs). Predicting survival outcomes for these patients with calibrated uncertainty is both challenging and essential for guiding subsequent treatments. This study introduces conformalized survival analysis (CSA) as a novel method for predicting survival times in critically ill HF patients. CSA enhances each predicted survival time with a statistically rigorous lower bound, providing valuable uncertainty quantification. Using the MIMIC-IV dataset, we demonstrate that CSA effectively delivers calibrated uncertainty quantification for survival predictions, in contrast to parametric models like the Cox or Accelerated Failure Time models. Through the application of CSA to a large, real-world dataset, this study underscores its potential to improve decision-making in critical care, offering a more precise and reliable tool for prognosis in a setting where accurate predictions and calibrated uncertainty can profoundly impact patient outcomes.