Predicting survival time for critically ill patients with heart failure using conformalized survival analysis
Wang, X.; Ren, Z.; Ye, J.
Show abstract
Heart failure (HF) is a serious public health issue, particularly for critically ill patients in intensive care units (ICUs). Predicting survival outcomes of critically ill patients with calibrated uncertainty calibration is a difficult yet crucially important task for timely treatment. This study applies a novel approach, conformalized survival analysis (CSA), to predicting the survival time to critically ill HF patients. CSA quantifies the uncertainty of point prediction by accompanying each predicted value with a lower bound guaranteed to cover the true survival time. Utilizing the MIMIC-IV dataset, we demonstrate that CSA delivers calibrated uncertainty quantification for the predicted survival time, while the methods based on parametric models (e.g., Cox model or the Accelerated Failure Time model) fail to do so. 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 calibrated uncertainty quantification can significantly influence patient outcomes.
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