Adapting Generative Large Language Models for Information Extraction from Unstructured Electronic Health Records in Residential Aged Care: A Comparative Analysis of Training Approaches

Authors

Yunshu Zhu (ORCID: https://orcid.org/0000-0003-2786-0775)
Mengyang Yin (ORCID: https://orcid.org/0000-0002-0212-4598)
Chao Deng (ORCID: https://orcid.org/0000-0003-1147-5741)
Mohammad Alkhalaf
Dinithi Vithanage (ORCID: https://orcid.org/0000-0001-5851-7158)
Ping Yu (ORCID: https://orcid.org/0000-0002-7910-9396)
Lei Wang (ORCID: https://orcid.org/0000-0002-0961-0441)
Zhenyu Zhang (ORCID: https://orcid.org/0000-0003-1853-4978)

Institutions

Qassim University (SA)
University of Wollongong (AU)
Adventist HealthCare (AU)

Publication Details

Journal: Journal of Healthcare Informatics Research
Published: 2025-02-20
DOI: https://doi.org/10.1007/s41666-025-00190-z
Citations: 10
Primary Topic: Machine Learning in Healthcare
Type: article
Field-Weighted Citation Impact: 24.81

Funders

National Computational Infrastructure
Australian Government
University Grants Commission
University of Wollongong
National Cancer Institute

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article

Adapting Generative Large Language Models for Information Extraction from Unstructured Electronic Health Records in Residential Aged Care: A Comparative Analysis of Training Approaches

Yunshu Zhu, Mengyang Yin, Chao Deng, Mohammad Alkhalaf et al.

Feb 20, 2025

10 citations

Journal of Healthcare Informatics Research

Machine Learning in Healthcare

24.81

article

Adapting Generative Large Language Models for Information Extraction from Unstructured Electronic Health Records in Residential Aged Care: A Comparative Analysis of Training Approaches

Yunshu Zhu, Mengyang Yin, Chao Deng, Mohammad Alkhalaf, Dinithi Vithanage, Ping Yu, Lei Wang, Zhenyu Zhang

February 20, 2025article en

10 citations

Abstract

No abstract available for this paper.

Journal of Healthcare Informatics ResearchVol. 9(2)

Qassim University (SA), University of Wollongong (AU), Adventist HealthCare (AU)

National Computational Infrastructure, Australian Government, University Grants Commission, University of Wollongong, National Cancer Institute

Openalex Percentile: Top 1%

Machine Learning in Healthcare

24.81

DOI Open Access PDF OpenAlex

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