Blood lactate concentration prediction in critical care patients: handling missing values

TL;DR

This paper introduces a benchmark problem for predicting blood lactate levels in ICU patients, emphasizing the importance of handling missing data to improve clinical decision-making and reduce invasive testing.

Contribution

It formally defines the lactate prediction problem as a benchmark, evaluates various algorithms, and analyzes the impact of missing value imputation methods on prediction accuracy.

Findings

Prediction algorithms show promising results.

Imputation methods significantly affect model performance.

Benchmark dataset facilitates future research in this area.

Abstract

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.