Predicting Diagnosis Code from Medication List of an Electronic Medical Record Using Convolutional Neural Network

Jakir Hossain Bhuiyan Masud, Ming Chin Lin

Research output: Contribution to journalArticle

Abstract

Automated coding and classification systems play a role in healthcare for quality of care. Our objective was to predict diagnosis code from medication list of electronic medical record (EMR) using convolutional neural network (CNN). We collected the clinical note from outpatient department (OPD) of Wanfang hospital, Taiwan of 2016 and used three physicians from three departments. The dataset was split into two parts, 90% for training and 10% for test cases. We used medication list as input and International Statistical Classification of Diseases 10 (ICD 10) code as output. After data preprocess, we used word2vector CNN to predict ICD 10 code. This study shows all the three physicians from three departments achieved better performance. The best performance of model was a physician from cardiology department achieved precision 69%, recall 89% and F measure 78%. We need to include more component such as text data, lab report for evaluation.

Original languageEnglish
Pages (from-to)1355-1356
Number of pages2
JournalStudies in Health Technology and Informatics
Volume270
DOIs
Publication statusPublished - Jun 16 2020

Keywords

  • Clinical note
  • convolutional neural network
  • International Statistical Classification of Diseases 10 (ICD 10)
  • medication

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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