2021
DOI: 10.1109/access.2021.3080085
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Transformers for Clinical Coding in Spanish

Abstract: Automatic clinical coding is an essential task in the process of extracting relevant information from unstructured documents contained in electronic health records (EHRs). However, most research in the development of computer-based methods for clinical coding focuses on texts written in English due to the limited availability of medical linguistic resources in languages other than English. With nearly 500 million native speakers, there is a worldwide interest in processing healthcare texts in Spanish. In this … Show more

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Cited by 22 publications
(12 citation statements)
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“…Yang et al [ 12 ] presented an LLM-based approach to clinical concept extraction. López-García et al [ 41 ] analyzed model performance of LLMs for automatic clinical coding in Spanish. Lentzen et al [ 5 ] studied LLM accuracy for automatic structuring of clinical notes in German.…”
Section: Discussionmentioning
confidence: 99%
“…Yang et al [ 12 ] presented an LLM-based approach to clinical concept extraction. López-García et al [ 41 ] analyzed model performance of LLMs for automatic clinical coding in Spanish. Lentzen et al [ 5 ] studied LLM accuracy for automatic structuring of clinical notes in German.…”
Section: Discussionmentioning
confidence: 99%
“…On the other hand, Metzger et al classified free-text clinical notes from ED relating to suicide attempts using Random Forest and Naive Bayes type algorithms. 23 Recent studies have shown the effectiveness of Transformers on classification tasks for EHR free-text data such as ICD coding 24,25 , phenotyping 26 or readmission prediction 27 . Therefore, within the framework of the TARPON project (Traitement Automatique des Résumés de Passage aux urgences dans le but de créer un Observatoire National), which aims to demonstrate the feasibility of setting up a national observatory of trauma, we propose here to compare the performances of several transformer models for the classification of ED visits for trauma based on clinical notes from the adult emergency department of the Bordeaux University Hospital.…”
Section: Related Workmentioning
confidence: 99%
“…By contrast, Metzger et al classified free-text clinical notes from ED related to suicide attempts using random forest and naive Bayes–type algorithms [ 23 ]. Recent studies have shown the effectiveness of transformers in classification tasks for EHR free-text data such as ICD coding [ 24 , 25 ], phenotyping [ 26 ], and readmission prediction [ 27 ]. Therefore, within the framework of the TARPON (Traitement Automatique des Résumés de Passage aux urgences dans le but de créer un Observatoire National) project, which aims to demonstrate the feasibility of setting up a national observatory of trauma, we propose here to compare the performances of several transformer models in the classification of ED visits for trauma based on clinical notes from the adult ED of the Bordeaux University Hospital.…”
Section: Introductionmentioning
confidence: 99%