ICU Mortality Prediction Using Long Short-Term Memory Networks

TL;DR

This paper explores the use of LSTM networks to analyze multivariate temporal data from ICU patients, aiming to predict in-hospital mortality and length of stay early with improved accuracy.

Contribution

It demonstrates the effectiveness of LSTM models on 6-hour time frames for real-time ICU mortality and LOS prediction using electronic health record data.

Findings

LSTM models outperform traditional methods in ICU prediction tasks.

6-hour data windows improve early prediction accuracy.

Multivariate time-series data enhances model performance.

Abstract

Extensive bedside monitoring in Intensive Care Units (ICUs) has resulted in complex temporal data regarding patient physiology, which presents an upscale context for clinical data analysis. In the other hand, identifying the time-series patterns within these data may provide a high aptitude to predict clinical events. Hence, we investigate, during this work, the implementation of an automatic data-driven system, which analyzes large amounts of multivariate temporal data derived from Electronic Health Records (EHRs), and extracts high-level information so as to predict in-hospital mortality and Length of Stay (LOS) early. Practically, we investigate the applicability of LSTM network by reducing the time-frame to 6-hour so as to enhance clinical tasks. The experimental results highlight the efficiency of LSTM model with rigorous multivariate time-series measurements for building real-world prediction engines.