Development of an Explainable Pre-Hospital Emergency Prediction Model for Acute Hospital Care

Abstract: This study introduces an eXplainable Artificial Intelligence (XAI) designed to predict which emergency patients require acute hospital care in pre-hospital phase and provide explanations for its reasoning. Emergency medical care is broadly divided into two stages: pre-hospital and in-hospital stages. Various information gathered during the emergency activities performed by paramedics in the pre-hospital stage and while transporting patients is crucial in describing the emergency patient’s condition. However, key pre-hospital information, important for the in-hospital medical care of emergency patients, is filtered based on the ambiguous memory of the paramedics, and is verbally shared in a condensed form via phone or radio when transmitted to the hospital. To address this issue, we have developed a model that predicts emergency patients based on pre-hospital information integrating an ensemble model and advanced XAI techniques. This proposed model not only predicts emergency situations requiring acute hospital care but also ensures the model's predictive processes remain transparent and interpretable for medical professionals, addressing the critical need for an information linkage system between the pre-hospital and in-hospital phases.

Keywords: eXplainable Artificial Intelligence (XAI), Pre-Hospital Emergency Prediction, Acute Hospital Care

DOI: 10.54941/ahfe1004646

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