Journal Article

FZJ-2024-01886

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals

Samadi, M. E. (Corresponding author) ; Guzman-Maldonado, J. ; Nikulina, K. ; Mirzaieazar, H. ; Sharafutdinov, K. ; Fritsch, S. J.FZJ* ; Schuppert, A.

2024
Macmillan Publishers Limited, part of Springer Nature [London]

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Please use a persistent id in citations: doi:10.1038/s41598-024-55577-6  doi:10.34734/FZJ-2024-01886

Abstract: The development of reliable mortality risk stratification models is an active research area in computational healthcare. Mortality risk stratification provides a standard to assist physicians in evaluating a patient’s condition or prognosis objectively. Particular interest lies in methods that are transparent to clinical interpretation and that retain predictive power once validated across diverse datasets they were not trained on. This study addresses the challenge of consolidating numerous ICD codes for predictive modeling of ICU mortality, employing a hybrid modeling approach that integrates mechanistic, clinical knowledge with mathematical and machine learning models . A tree-structured network connecting independent modules that carry clinical meaning is implemented for interpretability. Our training strategy utilizes graph-theoretic methods for data analysis, aiming to identify the functions of individual black-box modules within the tree-structured network by harnessing solutions from specific max-cut problems. The trained model is then validated on external datasets from different hospitals, demonstrating successful generalization capabilities, particularly in binary-feature datasets where label assessment involves extrapolation.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)
2. Center for Advanced Simulation and Analytics (CASA)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. SDI-S - SDI-S: Smart Data Innovation Services - Experimentelle Erprobung und Entwicklung von KI-Dienstverbünden für Innovationen auf industriellen Daten (01IS22095D) (01IS22095D)

Appears in the scientific report 2024

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