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. 2021 May 27:281:347-351.
doi: 10.3233/SHTI210178.

A Deep Learning Framework for Automated ICD-10 Coding

Abdelahad Chraibi  1 , David Delerue  1 , Julien Taillard  1 , Ismat Chaib Draa  1 , Régis Beuscart  2 , Arnaud Hansske  3
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Free article

A Deep Learning Framework for Automated ICD-10 Coding

Abdelahad Chraibi et al. Stud Health Technol Inform. .
Free article

Abstract

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.

Keywords: Automated Coding; Deep Learning; Medical informatics; Natural Language Processing.

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