A Deep Learning Framework for Automated ICD-10 Coding

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.

Exploration on the Practice of Translation Teaching for English Majors Based on the Internet of Things and Multimedia Assistance

“…The current English noun phrase recognition methods include machine translation-based recognition methods [1][2][3][4][5][6][7].…”