Aikodon, N, Ortega-Martorell, S and Olier-Caparroso, I (2023) Predicting Decompensation Risk in Intensive Care Unit Patients Using Machine Learning. Algorithms, 17 (1). ISSN 1999-4893

Preview

Text Predicting Decompensation Risk in Intensive Care Unit Patients.pdf - Published Version Available under License Creative Commons Attribution. Download (4MB) | Preview

Abstract

Patients in intensive care units (ICU) face the threat of decompensation, a rapid decline in health associated with a high risk of death. This study focuses on creating and evaluating machine learning (ML) models to predict decompensation risk in ICU patients. It proposes a novel approach using patient vitals and clinical data within a specified timeframe to forecast decompensation risk sequences. The study implemented and assessed long short-term memory (LSTM) and hybrid convolutional neural network (CNN)-LSTM architectures, along with traditional ML algorithms as baselines. Additionally, it introduced a novel decompensation score based on the predicted risk, validated through principal component analysis (PCA) and k-means analysis for risk stratification. The results showed that, with PPV=0.80, NPV=0.96 and AUC-ROC=0.90, CNN-LSTM had the best performance when predicting decompensation risk sequences. The decompensation score’s effectiveness was also confirmed (PPV=0.83 and NPV=0.96). SHAP plots were generated for the overall model and two risk strata, illustrating variations in feature importance and their associations with the predicted risk. Notably, this study represents the first attempt to predict a sequence of decompensation risks rather than single events, a critical advancement given the challenge of early decompensation detection. Predicting a sequence facilitates early detection of increased decompensation risk and pace, potentially leading to saving more lives

Item Type:	Article
Uncontrolled Keywords:	decompensation; risk prediction; intensive care unit; machine learning; deep learning; feature engineering; temporal data analysis; explainable artificial intelligence; clinical decision support; 01 Mathematical Sciences; 08 Information and Computing Sciences; 09 Engineering
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RA Public aspects of medicine
Divisions:	Computer Science & Mathematics
Publisher:	MDPI
SWORD Depositor:	A Symplectic
Date Deposited:	21 Dec 2023 10:59
Last Modified:	22 Jan 2024 14:15
DOI or ID number:	10.3390/a17010006
URI:	https://researchonline.ljmu.ac.uk/id/eprint/22139

View Item