Benchmarking machine learning models on multi-centre eICU critical care dataset

TL;DR

This paper introduces the first public benchmark suite for machine learning models on multi-centre critical care data, enabling standardized evaluation across four key clinical tasks using the eICU dataset.

Contribution

It establishes a comprehensive public benchmark for critical care ML models, comparing clinical standards with deep learning approaches across multiple tasks.

Findings

Deep learning models outperform clinical standards in several tasks.

Numerical variables significantly impact model performance.

Handling categorical variables affects prediction accuracy.

Abstract

Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and public benchmarks. Recent availability of large clinical datasets has enabled the possibility of establishing public benchmarks. Taking advantage of this opportunity, we propose a public benchmark suite to address four areas of critical care, namely mortality prediction, estimation of length of stay, patient phenotyping and risk of decompensation. We define each task and compare the performance of both clinical models as well as baseline and deep learning models using eICU critical care dataset of around 73,000 patients. This is the first public benchmark on a multi-centre critical care dataset, comparing the performance of clinical gold standard with our predictive model. We also investigate the impact of numerical variables as well as handling of categorical variables on each of the defined tasks. The source code, detailing our methods and experiments is publicly available such that anyone can replicate our results and build upon our work.