Summarizing Clinical Notes using LLMs for ICU Bounceback and Length-of-Stay Prediction

Recent advances in the Large Language Models (LLMs) provide a promising avenue for retrieving relevant information from clinical notes for accurate risk estimation of adverse patient outcomes. In this empirical study, we quantify the gain in predictive performance obtained by prompting LLMs to study the clinical notes and summarize potential risks for downstream tasks. Specifically, we prompt LLMs to generate a summary of progress notes and state potential complications that may arise. We then learn representations of the generated notes in sequential order and estimate the risks of patients in the ICU getting readmitted in ICU after discharge (ICU bouncebacks) and predict the overall length of stay in the ICU. Our analysis in the real-world MIMIC III dataset shows performance gains of 7.17% in terms of AUC-ROC and 14.16% in terms of AUPRC for the ICU bounceback task and 2.84% in terms of F-1 score and 7.12% in terms of AUPRC for the ICU LOS Prediction task. This demonstrates that the LLM-infused models outperform the approaches that only directly rely on clinical notes and other EHR data.