A Deep Learning Framework for Automated ...
Document type :
Article dans une revue scientifique: Article original
DOI :
PMID :
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Title :
A Deep Learning Framework for Automated ICD-10 Coding
Author(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]
Journal title :
Studies in Health Technology and Informatics
Abbreviated title :
Stud Health Technol Inform
Volume number :
281
Pages :
p. 347-351
Publication date :
2021
ISSN :
1879-8365
HAL domain(s) :
Sciences du Vivant [q-bio]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
Université de Lille
CHU Lille
CHU Lille
Submission date :
2023-11-15T06:32:03Z
2024-01-11T14:41:15Z
2024-01-11T14:41:15Z
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