GPT-4o and the quest for machine learning interpretability in ICU risk of death prediction

We introduce a novel approach to predicting in-ICU risk of death for mechanically ventilated patients using a GPT hybrid model. Our methodology demonstrates the potential of integrating large language models with traditional machine learning techniques to create interpretable and clinically relevant predictive models.

Authors

Kateryna Nikulina
Andreas Schuppert (ORCID: https://orcid.org/0000-0003-3783-6605)
Sebastian Fritsch (ORCID: https://orcid.org/0000-0002-8350-8584)
Moein E. Samadi (ORCID: https://orcid.org/0000-0002-3916-1085)

Institutions

Forschungszentrum Jülich (DE)
RWTH Aachen University (DE)

Publication Details

Journal: BMC Medical Informatics and Decision Making
Published: 2025-10-13
DOI: https://doi.org/10.1186/s12911-025-03224-z
Primary Topic: Machine Learning in Healthcare
Type: article
Field-Weighted Citation Impact: 0.00

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article

GPT-4o and the quest for machine learning interpretability in ICU risk of death prediction

Kateryna Nikulina, Andreas Schuppert, Sebastian Fritsch, Moein E. Samadi

Oct 13, 2025

BMC Medical Informatics and Decision Making

Machine Learning in Healthcare

article

GPT-4o and the quest for machine learning interpretability in ICU risk of death prediction

Kateryna Nikulina, Andreas Schuppert, Sebastian Fritsch, Moein E. Samadi

October 13, 2025article en

Abstract

We introduce a novel approach to predicting in-ICU risk of death for mechanically ventilated patients using a GPT hybrid model. Our methodology demonstrates the potential of integrating large language models with traditional machine learning techniques to create interpretable and clinically relevant predictive models.

BMC Medical Informatics and Decision MakingVol. 25(1)

Forschungszentrum Jülich (DE), RWTH Aachen University (DE)

Openalex Percentile: Top 85%

Machine Learning in Healthcare

DOI Open Access PDF OpenAlex

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