Detecting Uncertainty of Mortality Prediction Using Confident Learning

Early mortality prediction is an actively researched problem that has led to the development of various severity scores and machine learning (ML) models for accurate and reliable detection of mortality in severely ill patients staying in intensive care units (ICUs). However, the uncertainty of such predictions due to irregular patient sampling, missing information, or high diversity of patient data has not yet been adequately addressed. In this paper, we used confident learning (CL) to incorporate sample-uncertainty information into our mortality prediction models and evaluated the performance of these models using a large dataset of 139,367 unique ICU admissions within the eICU Collaborative Research Database (eICU-CRD). The results of our study validate the importance of uncertainty quantification in patient outcome prediction and show that the state-of-the-art ML models augmented with CL are more robust against epistemic error and class imbalance.