Automated ICD coding using extreme multi-label long text transformer-based models-Reference-Cited by-同舟云学术

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Automated ICD coding using extreme multi-label long text transformer-based models

Published:2023-10 Issue: Volume:144 Page:102662
ISSN:0933-3657
Container-title:Artificial Intelligence in Medicine
language:en
Short-container-title:Artificial Intelligence in Medicine

Author:

Liu Leibo,Perez-Concha Oscar,Nguyen Anthony,Bennett Vicki,Jorm Louisa

Funder

Australian Government

Commonwealth Scientific and Industrial Research Organisation

Australian Government Department of Education

Publisher

Elsevier BV

Subject

Artificial Intelligence,Medicine (miscellaneous)

Reference47 articles.

1. Experimental evaluation and development of a silver-standard for the MIMIC-III clinical coding dataset;Searle,2020

2. ICD-11: an international classification of diseases for the twenty-first century;Harrison;BMC Med Inform Decis Mak,2021

3. Problems and barriers during the process of clinical coding: a focus group study of coders’ perceptions;Alonso;J Med Syst,2020

4. The increasing importance of clinical coding;Tatham;Br J Hosp Med,2008

5. A systematic literature review of automated clinical coding and classification systems;Stanfill;J Am Med Inform Assoc,2010

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4. Prediction of American Society of Anesthesiologists Physical Status Classification from preoperative clinical text narratives using natural language processing;BMC Anesthesiology;2023-09-04

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