The integrated multiple event representation framework (IMERF): a case study on critically-ill patients

Olier, I ORCID: 0000-0002-5679-7501, Ortega-Martorell, S ORCID: 0000-0001-9927-3209, Margereson, G ORCID: 0009-0004-5117-4032, Bellfield, RAA ORCID: 0000-0002-9945-914X, Welters, ID and Lip, GYH ORCID: 0000-0002-7566-1626 (2025) The integrated multiple event representation framework (IMERF): a case study on critically-ill patients. Computers in Biology and Medicine, 198. p. 111196. ISSN 0010-4825

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Abstract

This study introduces the Integrated Multiple Event Representation Framework (IMERF), a novel methodological approach for developing risk prediction models for multiple clinical events. Using a two-stage process involving multi-task learning and dimensionality reduction, IMERF creates a visual representation of predicted event risks and identifies clusters based on overlapping risks. The proposed framework is showcased through a case study modelling nine adverse events in critically ill patients admitted to intensive care units (ICUs). Stage 1 was implemented using convolutional neural networks, which displayed superior performance to logistic regression and random forest algorithms. The generative topographic mapping (GTM) algorithm was implemented in stage 2 for data visualisation and clustering. It revealed clear patterns of adverse event risk clusters. GTM in combination with class activation maps was also employed to trace input factors influencing cluster membership, highlighting distinct risk profiles among patients. Macro-clusters representing distinctive combinations of adverse event risk levels were also identified by performing a hierarchical clustering on the GTM results. In conclusion, IMERF could represent a significant advancement in multiple event risk modelling by enabling simultaneous prediction and characterisation of overlapping events and providing an interpretable framework for understanding their complex patterns. Its application in ICUs underscores its potential for broader clinical use, including modelling clusters of conditions or multiple instances of events.

Item Type:	Article
Uncontrolled Keywords:	46 Information and Computing Sciences; 4611 Machine Learning; Machine Learning and Artificial Intelligence; Networking and Information Technology R&D (NITRD); Patient Safety; Generic health relevance; 08 Information and Computing Sciences; 09 Engineering; 11 Medical and Health Sciences; Biomedical Engineering; 3102 Bioinformatics and computational biology; 4203 Health services and systems; 4601 Applied computing
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine R Medicine > RT Nursing
Divisions:	Computer Science and Mathematics Nursing and Advanced Practice
Publisher:	Elsevier BV
Date of acceptance:	6 October 2025
Date of first compliant Open Access:	20 October 2025
Date Deposited:	20 Oct 2025 09:09
Last Modified:	20 Oct 2025 09:15
DOI or ID number:	10.1016/j.compbiomed.2025.111196
URI:	https://researchonline.ljmu.ac.uk/id/eprint/27373

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