Leveraging a large language model (LLM) to predict hospital admissions of emergency department patients

• An LLM-based approach for predicting Hospital Admission of ED patients. • Leveraging story telling in Predictive Process Monitoring. • Exploring the performance of fine-tuning several foundation LLM models. • Experiments with the real event log MIMICEL. • Explanations of predictions are provided using Integrated Gradients. An Emergency Department (ED) is a hospital facility that is staffed 24 hours a day, 7 days a week, and provides unscheduled services to patients whose health status requires immediate care. The management of the hospital admissions is one of the most critical processes for an ED. Several literature studies have recently used Artificial Intelligence (AI) methods to predict hospital admissions of ED patients. However, they generally handle ED journey features recording data with “one” multiplicity per journey. They often consider patients’ data collected at the end of ED journeys by rarely paying attention to obtain accurate decisions already during the early stages of ED journeys. Finally, they commonly use AI methods without equipping AI models with explanations of model decisions. Instead, this study illustrates an explainable Predictive Process Monitoring (PPM) method, called LEGOLAS , that uses a Large Language model (LLM) to obtain accurate and explainable decisions regarding the hospital admission of an ED patient. Decisions are already obtained in the early stages of an ED patient journey. A contribution of this study is the use of a story telling to describe ED patient journeys. This story telling accounts for the variety and multiplicity of information commonly recorded in free events logged for ED patients without requiring any complex preprocessing. Another contribution is the exploration of the accuracy performance of several foundation LLMs used with fine-tuning for the ED patient management process. A further contribution is an evaluation study with the real-life event log MIMICEL , to explore the impact and significance of the proposed method in terms of accuracy and earliness of decisions. This evaluation shows that LEGOLAS achieves overall accuracy equal to 0.76 by outperforming related AI methods that stop at an accuracy of 0.66. In addition, LEGOLAS achieves Fscore greater than 0.80 for the two majority classes of the study – Admitted and Home – after only twelve events observed in the ED patient journey. Finally, the evaluation explains which events and which categories of information better contribute to reveal the outcome of running ED patient journeys.