A Deep Learning Framework for Automated ...
Type de document :
Article dans une revue scientifique: Article original
DOI :
PMID :
URL permanente :
Titre :
A Deep Learning Framework for Automated ICD-10 Coding
Auteur(s) :
Chraibi, A. [Auteur]
Delerue, D. [Auteur]
Taillard, J. [Auteur]
Chaib Draa, I. [Auteur]
Beuscart, Regis [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Hansske, A. [Auteur]
Delerue, D. [Auteur]
Taillard, J. [Auteur]
Chaib Draa, I. [Auteur]
Beuscart, Regis [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Hansske, A. [Auteur]
Titre de la revue :
Studies in Health Technology and Informatics
Nom court de la revue :
Stud Health Technol Inform
Numéro :
281
Pagination :
p. 347-351
Date de publication :
2021
ISSN :
1879-8365
Discipline(s) HAL :
Sciences du Vivant [q-bio]
Résumé en anglais : [en]
The International Statistical Classification of Diseases and Related Health Problems (ICD) is one of the widely used classification system for diagnoses and procedures to assign diagnosis codes to Electronic Health Record ...
Lire la suite >The International Statistical Classification of Diseases and Related Health Problems (ICD) is one of the widely used classification system for diagnoses and procedures to assign diagnosis codes to Electronic Health Record (EHR) associated with a patient’s stay. The aim of this paper is to propose an automated coding system to assist physicians in the assignment of ICD codes to EHR. For this purpose, we created a pipeline of Natural Language Processing (NLP) and Deep Learning (DL) models able to extract the useful information from French medical texts and to perform classification. After the evaluation phase, our approach was able to predict 346 diagnosis codes from heterogeneous medical units with an accuracy average of 83%. Our results were finally validated by physicians of the Medical Information Department (MID) in charge of coding hospital stays.Lire moins >
Lire la suite >The International Statistical Classification of Diseases and Related Health Problems (ICD) is one of the widely used classification system for diagnoses and procedures to assign diagnosis codes to Electronic Health Record (EHR) associated with a patient’s stay. The aim of this paper is to propose an automated coding system to assist physicians in the assignment of ICD codes to EHR. For this purpose, we created a pipeline of Natural Language Processing (NLP) and Deep Learning (DL) models able to extract the useful information from French medical texts and to perform classification. After the evaluation phase, our approach was able to predict 346 diagnosis codes from heterogeneous medical units with an accuracy average of 83%. Our results were finally validated by physicians of the Medical Information Department (MID) in charge of coding hospital stays.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
Université de Lille
CHU Lille
CHU Lille
Date de dépôt :
2023-11-15T06:32:03Z
2024-01-11T14:41:15Z
2024-01-11T14:41:15Z
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