Blood lactate concentration prediction in critical care patients: handling missing values
This work addresses a critical care issue by providing a benchmark for machine learning to assist in clinical decision-making, though it appears incremental in its focus on imputation methods.
The paper tackles the problem of predicting blood lactate concentration in ICU patients to reduce invasive testing, and demonstrates promising results by evaluating various prediction algorithms and missing value imputation methods on a large eICU dataset.
Blood lactate concentration is a strong indicator of mortality risk in critically ill patients. While frequent lactate measurements are necessary to assess patient's health state, the measurement is an invasive procedure that can increase risk of hospital-acquired infections. For this reason we formally define the problem of lactate prediction as a clinically relevant benchmark problem for machine learning community so as to assist clinical decision making in blood lactate testing. Accordingly, we demonstrate the relevant challenges of the problem and its data in addition to the adopted solutions. Also, we evaluate the performance of different prediction algorithms on a large dataset of ICU patients from the multi-centre eICU database. More specifically, we focus on investigating the impact of missing value imputation methods in lactate prediction for each algorithm. The experimental analysis shows promising prediction results that encourages further investigation of this problem.