Transformer-based model for predicting length of stay in intensive care unit in sepsis patients

Transformer-based model for predicting length of stay in intensive care unit in sepsis patients

IntroductionSepsis, a life-threatening condition with a high mortality rate, requires intensive care unit (ICU) admission. The increasing hospitalization rate for patients with sepsis has escalated medical costs due to the strain on ICU resources. Efficient management of ICU resources is critical to...

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Main Authors: Jeesu Kim, Geun-Hyeong Kim, Jae-Woo Kim, Ka Hyun Kim, Jae-Young Maeng, Yong-Goo Shin, Seung Park
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Medicine
Subjects:
sepsis
intensive care unit
length of stay
sequential organ failure assessment
transformer
tabular data
Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2024.1473533/full
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Summary:IntroductionSepsis, a life-threatening condition with a high mortality rate, requires intensive care unit (ICU) admission. The increasing hospitalization rate for patients with sepsis has escalated medical costs due to the strain on ICU resources. Efficient management of ICU resources is critical to addressing this challenge.MethodsThis study utilized the dataset collected from 521 patients with sepsis at Chungbuk National University Hospital between July 2020 and August 2023. A transformer-based deep learning model was developed to predict ICU length of stay (LOS). The model incorporated global and local input data analysis through classification and feature-wise tokens, based on sequential organ failure assessment (SOFA) criteria. Model performance was evaluated using four-fold cross-validation.ResultsThe proposed model achieved a mean absolute error (MAE) of 2.05 days for predicting ICU LOS. The result demonstrates the ability of the proposed model to provide accurate and reliable predictions.DiscussionThe proposed model offers valuable insights for healthcare resource management by optimizing ICU resource allocation and potentially reducing medical expenses. These findings highlight the applicability of the proposed model to efficient healthcare cost management.
ISSN:2296-858X

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