Chraibi Abdelahad, Delerue David, Taillard Julien, Chaib Draa Ismat, Beuscart Régis, Hansske Arnaud
ALICANTE SARL, France.
ULR2694, Lille University, France.
Stud Health Technol Inform. 2021 May 27;281:347-351. doi: 10.3233/SHTI210178.
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.
《国际疾病及相关健康问题统计分类》（ICD）是广泛用于诊断和程序的分类系统之一，用于为与患者住院相关的电子健康记录（EHR）分配诊断代码。本文的目的是提出一种自动编码系统，以协助医生为EHR分配ICD代码。为此，我们创建了一个自然语言处理（NLP）和深度学习（DL）模型管道，能够从法语医学文本中提取有用信息并进行分类。在评估阶段之后，我们的方法能够从不同的医疗单位预测346个诊断代码，平均准确率为83%。我们的结果最终得到了负责为住院编码的医学信息部（MID）医生的验证。
