Article

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

2025-01-20

Abstract excerpt

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 su...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 8fd9d1be-42b9-50d6-ac6f-fd2712585c07
DOI: 10.1101/2025.01.19.25320797

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Summarizing Clinical Notes using LLMs for ICU Bounceback and Length-of-Stay PredictionDOI 10.1101/2025.01.19.25320797Read

Toward Digital Twins in the Intensive Care Unit: A Medication Management Case StudySemantic proximity Read

Large-Language-Model Mortality Risk Stratification in the Intensive Care Unit: A Benchmark Against APACHE IISemantic proximity Read

Large Language Models Improve Cancer Survival Prediction Using Real-World Clinical NotesSemantic proximity Read

A real-world feasibility evaluation of LLM-based clinical prediction: emergency department return visit admission across two academic medical centersSemantic proximity Read

Fine-Tuning LLaMA2 for Summarizing Discharge Notes: Evaluating the Role of Highlighted InformationSemantic proximity Read

LCD Benchmark: Long Clinical Document Benchmark on Mortality Prediction for Language ModelsSemantic proximity Read

Combining structured and unstructured data for predictive models: a deep learning approachSemantic proximity Read

Evaluating Large Language Models for Drafting Emergency Department Discharge SummariesSemantic proximity Read

Physician- versus Large Language Model-Generated Clinical Summaries in the Emergency DepartmentSemantic proximity Read

Synthetic Data for Accessible Learning in Healthcare: Improving Mortality Prediction with Longitudinal DataSemantic proximity Read

Sentiment in Clinical Notes: A Predictor for Length of Stay?Semantic proximity Read

MIMIC-IV-Ext-22MCTS: A 22 Millions-Event Temporal Clinical Time-Series Dataset with Relative Timestamp for Risk PredictionSemantic proximity Read