Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies
Background/Objectives: This study develops machine learning (ML) models to predict hypoxemia severity during emergency triage, particularly in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) scenarios, using physiological data from medical-grade sensors. Methods: Tree-based models...
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MDPI AG
2024-12-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/23/2763 |
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| author | Santino Nanini Mariem Abid Yassir Mamouni Arnaud Wiedemann Philippe Jouvet Stephane Bourassa |
| author_facet | Santino Nanini Mariem Abid Yassir Mamouni Arnaud Wiedemann Philippe Jouvet Stephane Bourassa |
| author_sort | Santino Nanini |
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| description | Background/Objectives: This study develops machine learning (ML) models to predict hypoxemia severity during emergency triage, particularly in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) scenarios, using physiological data from medical-grade sensors. Methods: Tree-based models (TBMs) such as XGBoost, LightGBM, CatBoost, Random Forests (RFs), Voting Classifier ensembles, and sequential models (LSTM, GRU) were trained on the MIMIC-III and IV datasets. A preprocessing pipeline addressed missing data, class imbalances, and synthetic data flagged with masks. Models were evaluated using a 5-min prediction window with minute-level interpolations for timely interventions. Results: TBMs outperformed sequential models in speed, interpretability, and reliability, making them better suited for real-time decision-making. Feature importance analysis identified six key physiological variables from the enhanced NEWS2+ score and emphasized the value of mask and score features for transparency. Voting Classifier ensembles showed slight metric gains but did not outperform individually optimized models, facing a precision-sensitivity tradeoff and slightly lower F1-scores for key severity levels. Conclusions: TBMs were effective for real-time hypoxemia prediction, while sequential models, though better at temporal handling, were computationally costly. This study highlights ML’s potential to improve triage systems and reduce alarm fatigue, with future plans to incorporate multi-hospital datasets for broader applicability. |
| format | Article |
| id | doaj-art-d707a8a2dd094e28a6b22457bbdd120d |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Diagnostics |
| spelling | doaj-art-d707a8a2dd094e28a6b22457bbdd120d2025-08-20T02:38:43ZengMDPI AGDiagnostics2075-44182024-12-011423276310.3390/diagnostics14232763Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE EmergenciesSantino Nanini0Mariem Abid1Yassir Mamouni2Arnaud Wiedemann3Philippe Jouvet4Stephane Bourassa5Clinical Decision Support System Articificial Intelligence Health Cluster in Acute Child Care, PE-DIATRICS, CHU Ste-Justine Centre Hospitalier Universitaire Mère-Enfant, 3175 Boulevard de la Côte-Sainte-Catherine Drive, Montréal, QC H3T 1C5, CanadaClinical Decision Support System Articificial Intelligence Health Cluster in Acute Child Care, PE-DIATRICS, CHU Ste-Justine Centre Hospitalier Universitaire Mère-Enfant, 3175 Boulevard de la Côte-Sainte-Catherine Drive, Montréal, QC H3T 1C5, CanadaFaculté des arts et des sciences, Département d’informatique et de recherche opérationnelle (DIRO), Université de Montréal, 3150 Rue Jean-Brillant, Montréal, QC H3T 1N8, CanadaResearch Center CHU Ste-Justine Centre Hospitalier Universitaire Mère-Enfant, 3175 Boulevard de la Côte-Sainte-Catherine Drive, Montréal, QC H3T 1C5, CanadaResearch Center CHU Ste-Justine Centre Hospitalier Universitaire Mère-Enfant, 3175 Boulevard de la Côte-Sainte-Catherine Drive, Montréal, QC H3T 1C5, CanadaResearch Center CHU Ste-Justine Centre Hospitalier Universitaire Mère-Enfant, 3175 Boulevard de la Côte-Sainte-Catherine Drive, Montréal, QC H3T 1C5, CanadaBackground/Objectives: This study develops machine learning (ML) models to predict hypoxemia severity during emergency triage, particularly in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) scenarios, using physiological data from medical-grade sensors. Methods: Tree-based models (TBMs) such as XGBoost, LightGBM, CatBoost, Random Forests (RFs), Voting Classifier ensembles, and sequential models (LSTM, GRU) were trained on the MIMIC-III and IV datasets. A preprocessing pipeline addressed missing data, class imbalances, and synthetic data flagged with masks. Models were evaluated using a 5-min prediction window with minute-level interpolations for timely interventions. Results: TBMs outperformed sequential models in speed, interpretability, and reliability, making them better suited for real-time decision-making. Feature importance analysis identified six key physiological variables from the enhanced NEWS2+ score and emphasized the value of mask and score features for transparency. Voting Classifier ensembles showed slight metric gains but did not outperform individually optimized models, facing a precision-sensitivity tradeoff and slightly lower F1-scores for key severity levels. Conclusions: TBMs were effective for real-time hypoxemia prediction, while sequential models, though better at temporal handling, were computationally costly. This study highlights ML’s potential to improve triage systems and reduce alarm fatigue, with future plans to incorporate multi-hospital datasets for broader applicability.https://www.mdpi.com/2075-4418/14/23/2763hypoxemiamachine learningpatient triagedisaster managementCBRNE eventsVIMY Multi-System |
| spellingShingle | Santino Nanini Mariem Abid Yassir Mamouni Arnaud Wiedemann Philippe Jouvet Stephane Bourassa Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies Diagnostics hypoxemia machine learning patient triage disaster management CBRNE events VIMY Multi-System |
| title | Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies |
| title_full | Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies |
| title_fullStr | Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies |
| title_full_unstemmed | Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies |
| title_short | Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies |
| title_sort | machine and deep learning models for hypoxemia severity triage in cbrne emergencies |
| topic | hypoxemia machine learning patient triage disaster management CBRNE events VIMY Multi-System |
| url | https://www.mdpi.com/2075-4418/14/23/2763 |
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