Large Language Models Can Effectively Extract Stroke and Reperfusion Audit Data from Medical Free-Text Discharge Summaries-Reference-Cited by-同舟云学术

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Large Language Models Can Effectively Extract Stroke and Reperfusion Audit Data from Medical Free-Text Discharge Summaries

Published:2024 Issue: Volume: Page:
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Author:

Goh Rudy,Cook Benjamin Joseph,Stretton Brandon,Booth Andrew,Satheakeerthy Shrirajh,Howard Sarah,Kovoor Joshua,Gupta Aashray,Tan Sheryn,Kimberley W. Taylor,Moey Andrew,Vallat Wilson,Maddison John,Marks Jarrod,Gluck Samuel,Gilbert Toby,Jannes Jim,Kleinig Timothy,Bacchi Stephen

Publisher

Elsevier BV

Reference11 articles.

1. Clinical audits: Why and for whom;M Boult;ANZ J Surg,2007

2. Automated information extraction from free-text medical documents for stroke key performance indicators: a pilot study;S Bacchi;Intern Med J,2022

3. This too shall pass: the performance of ChatGPT-3.5, ChatGPT-4 and New Bing in an Australian medical licensing examination;O Kleinig;Medical Journal of Australia,2023

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