Leveraging large language models to predict unplanned ICU readmissions from electronic health records

Unplanned readmissions to Intensive Care Units (ICUs) are associated with increased mortality, higher healthcare costs, and significant strain on limited medical resources. Accurate prediction of readmissions can improve patient outcomes and optimize resource allocation. This study investigates the use of large language models (LLMs) for ICU readmission prediction through both classification and explanation tasks. We compare a general-purpose model (Gemma2B) and a medical-domain model (Apollo2B), both open-source and fine-tuned for this task. The models were evaluated on their ability to classify readmission cases and generate clinically meaningful justifications. Gemma2B outperformed Apollo2B, achieving an AUC of 0.9, along with strong performance in explanatory outputs. Its ability to produce accurate, context-aware explanations without hallucinations underscores the value of fine-tuned general-purpose models in healthcare settings. These findings highlight the promise of interpretable LLMs in critical care and support their integration into clinical workflows to enhance patient safety and reduce the burden of unplanned ICU readmissions. • Utilized Large Language Models (LLMs) to predict unplanned ICU re-admission from Electronic Health Records (EHRs). • Developed a serialization approach to transform structured EHR data into a text-based format for LLM processing. • Investigated explicit classification (binary labels) and implicit classification (text generation) to enhance interpretability. • Demonstrated the potential of LLMs in clinical decision support by generating interpretable insights for ICU physicians.