PreprintArticleVersion 1This version is not peer-reviewed
Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices
Version 1
: Received: 2 November 2024 / Approved: 4 November 2024 / Online: 4 November 2024 (09:30:06 CET)
How to cite:
Nanini, S.; Abid, M.; Mamouni, Y.; Wiedemann, A.; Jouvet, P.; Bourassa, S. Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices. Preprints2024, 2024110168. https://doi.org/10.20944/preprints202411.0168.v1
Nanini, S.; Abid, M.; Mamouni, Y.; Wiedemann, A.; Jouvet, P.; Bourassa, S. Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices. Preprints 2024, 2024110168. https://doi.org/10.20944/preprints202411.0168.v1
Nanini, S.; Abid, M.; Mamouni, Y.; Wiedemann, A.; Jouvet, P.; Bourassa, S. Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices. Preprints2024, 2024110168. https://doi.org/10.20944/preprints202411.0168.v1
APA Style
Nanini, S., Abid, M., Mamouni, Y., Wiedemann, A., Jouvet, P., & Bourassa, S. (2024). Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices. Preprints. https://doi.org/10.20944/preprints202411.0168.v1
Chicago/Turabian Style
Nanini, S., Philippe Jouvet and Stephane Bourassa. 2024 "Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices" Preprints. https://doi.org/10.20944/preprints202411.0168.v1
Abstract
This paper presents the development of machine learning (ML) models to predict hypoxemia severity during emergency triage, especially in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) events, using physiological data from medical-grade sensors. Gradient Boosting Models (XGBoost, LightGBM, CatBoost) and sequential models (LSTM, GRU) were trained on physiological and demographic data from the MIMIC-III and IV datasets. A robust preprocessing pipeline addressed missing data, class imbalances, and incorporated synthetic data flagged with masks.
Gradient Boosting Models (GBMs) outperformed sequential models in terms of training speed, interpretability, and reliability, making them well-suited for real-time decision-making. While their performance was comparable to that of sequential models, the GBMs used score features from six physiological variables derived from the enhanced National Early Warning Score (NEWS) 2, which we termed NEWS2+. This approach significantly improved prediction accuracy.
While sequential models handled temporal data well, their performance gains didn’t justify the higher computational cost. A 5-minute prediction window was chosen for timely intervention, with minute-level interpolations standardizing the data.
Feature importance analysis highlighted the significant role of mask and score features in enhancing both transparency and performance. Temporal dependencies proved to be less critical, as Gradient Boosting Models were able to capture key patterns effectively without relying on them.
This study highlights ML's potential to improve triage and reduce alarm fatigue. Future work will integrate data from multiple hospitals to enhance model generalizability across clinical settings.
Keywords
hypoxemia; machine learning; patient triage; disaster management; CBRNE events; VIMY Multi-System; Gradient Boosting Models; NEWS2+; data preprocessing; feature importance; LSTM; GRU; time series interpolation; deep learning; imputation; interpolation; sliding window; masks; early warning scores; EWS; artificial intelligence; XGBoost; CatBoost; LightGBM
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
