Conference proceeding
Summarizing Clinical Notes using LLMs for ICU Bounceback and Length-of-Stay Prediction
IEEE ... International Conference on Data Mining workshops, pp.859-866
12/09/2024
DOI: 10.1109/ICDMW65004.2024.00118
Abstract
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.
Details
- Title: Subtitle
- Summarizing Clinical Notes using LLMs for ICU Bounceback and Length-of-Stay Prediction
- Creators
- Akash Choudhuri - University of IowaPhilip Polgreen - University of IowaAlberto Segre - University of IowaBijaya Adhikari - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- IEEE ... International Conference on Data Mining workshops, pp.859-866
- DOI
- 10.1109/ICDMW65004.2024.00118
- eISSN
- 2375-9259
- Publisher
- IEEE
- Grant note
- Health (10.13039/100018696)
- Language
- English
- Date published
- 12/09/2024
- Academic Unit
- Infectious Diseases; Epidemiology; Nursing; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Computer Science; Internal Medicine
- Record Identifier
- 9984802409302771
Metrics
6 Record Views