The Efficiency of Artificial Intelligence in Predicting Hospitalization for Trauma Patients
Predicting Hospitalization for Trauma Patients
Keywords:
emergency room, artificial intelligence, trauma, hospitalizationAbstract
Overcrowding in emergency departments presents a significant challenge at Nan Hospital. This retrospective observational study evaluated the efficiency of artificial intelligence in predicting hospital admission among accident patients. Data from accident cases recorded in the hospital's electronic database during 2022 were utilized to develop AI models using machine learning, specifically decision trees. Subsequently, data from January 1, 2023 to April 30, 2023 were employed to validate the AI model. The predictive performance of the AI was statistically assessed using a confusion matrix. The model predicted outcomes for 3,185 accident patients, achieving an overall accuracy of 92.3%, sensitivity of 89.8%, and specificity of 93.4%. Notably, for resuscitation, emergency, and semi-urgent categories, the model demonstrated 100% accuracy, while the urgency category achieved 91% accuracy. These findings indicated that AI has potential to effectively predict hospital admission for accident patients and can be used in supporting clinical decision-making and potentially mitigating emergency department overcrowding. AI-driven prediction systems could enhance patient care efficiency by enabling better resource allocation and prioritization in critical care settings
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