Comorbidity Extraction for In-Hospital Mortality Analysis: a Comparison of Regular Expressions and Large Language Models

Limited hospital resources may prolong patient stays in the Emergency Department (ED), potentially affecting clinical outcomes. This paper investigates the link between overnight Emergency Department (ED) stays and in-hospital mortality, focusing on comorbidity extraction from clinical records. In Italian healthcare records, comorbidities are typically documented using abbreviations and non-standard clinical slang in unstructured free-text fields. We evaluated two approaches for comorbidity extraction: a rule-based method and a Large Language Model approach. Both were assessed against a dataset of 200 clinical records manually annotated by emergency medical staff. This first result showed that the rule-based strategy outperformed Large Language Models in terms of recall, F1-score, consistency, and reliability. Then, to assess the impact of overnight stays on in-hospital mortality and to identify the most significant predictors, 126,696 ED admissions at the Romagna Local Health Agency in Forlì, Italy, between 2017 and 2022 were analysed using several models, with particular emphasis on interpretability. Comorbidity burden, diagnosis severity, age, and infectious, respiratory, and circulatory diseases emerged as the most influential factors.