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

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