Faculty, Staff and Student Publications
Publication Date
3-1-2024
Journal
Journal of Biomedical Informatics
Abstract
Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucial for enabling healthcare providers to make well-informed decisions regarding patient care. Summarizing clinical notes further assists healthcare professionals in pinpointing potential health risks and making better-informed decisions. This process contributes to reducing errors and enhancing patient outcomes by ensuring providers have access to the most pertinent and current patient data. Recent research has shown that incorporating instruction prompts with large language models (LLMs) substantially boosts the efficacy of summarization tasks. However, we show that this approach also leads to increased performance variance, resulting in significantly distinct summaries even when instruction prompts share similar meanings. To tackle this challenge, we introduce a model-agnostic Soft Prompt-BasedCalibration (SPeC) pipeline that employs soft prompts to lower variance while preserving the advantages of prompt-based summarization. Experimental findings on multiple clinical note tasks and LLMs indicate that our method not only bolsters performance but also effectively regulates variance across different LLMs, providing a more consistent and reliable approach to summarizing critical medical information.
Keywords
Humans, Calibration, Natural Language Processing, Electronic Health Records, Language, Health Personnel, Natural language processing, Knowledge representation and information modeling, Data mining, Machine learning, Clinical research informatics
DOI
10.1016/j.jbi.2024.104606
PMID
38325698
PMCID
PMC11608453
PubMedCentral® Posted Date
12-1-2024
PubMedCentral® Full Text Version
Author MSS
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Data Science Commons, Medical Sciences Commons