Infectious diseases now · 2026
Automated triage in pandemics: Support vector machine for efficient patient classification.
СтатьяНазвание и аннотация: Перевод в очереди
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Gallardo-Hernández Ana Gabriela, Colín-Martínez Tania, González-Olvera Marcos A, Escobar Jesica, Flores-Pérez Anahi, Hernández Debbie, Vázquez-Orduña Eli Abraham
Аннотация
INTRODUCTION: We aimed to develop tools to ease the burden on medical staff while improving patient care. The central question is whether Support Vector Machines (SVMs), trained solely on patients' self-reported data (symptoms, demographics, comorbidities), can achieve triage accuracy comparable to or surpassing traditional score-based systems in emergency departments under pandemic conditions.
METHODS: We propose an SVM-based approach to replicate COVID-19 triage using patient-provided information rather than laboratory tests. The system automates triage scoring, classifying patients into red, yellow, or green categories. A computer program records patient data and calculates scores, while physicians have the ability to override results based on their clinical judgment.
RESULTS: The system integrates clinical expertise, enhancing rather than replacing physicians' roles. It achieved over 98% accuracy in red-triage classification using less than 2.8% of training data.
CONCLUSIONS: This SVM-based triage model shows promise for improving emergency department processes, accuracy, and patient outcomes in high-demand situations.
METHODS: We propose an SVM-based approach to replicate COVID-19 triage using patient-provided information rather than laboratory tests. The system automates triage scoring, classifying patients into red, yellow, or green categories. A computer program records patient data and calculates scores, while physicians have the ability to override results based on their clinical judgment.
RESULTS: The system integrates clinical expertise, enhancing rather than replacing physicians' roles. It achieved over 98% accuracy in red-triage classification using less than 2.8% of training data.
CONCLUSIONS: This SVM-based triage model shows promise for improving emergency department processes, accuracy, and patient outcomes in high-demand situations.