Natural Language Processing of Radiology Text Reports: Interactive Text Classification.

Journal Article (Journal Article)

This report presents a hands-on introduction to natural language processing (NLP) of radiology reports with deep neural networks in Google Colaboratory (Colab) to introduce readers to the rapidly evolving field of NLP. The implementation of the Google Colab notebook was designed with code hidden to facilitate learning for noncoders (ie, individuals with little or no computer programming experience). The data used for this module are the corpus of radiology reports from the Indiana University chest x-ray collection available from the National Library of Medicine's Open-I service. The module guides learners through the process of exploring the data, splitting the data for model training and testing, preparing the data for NLP analysis, and training a deep NLP model to classify the reports as normal or abnormal. Concepts in NLP, such as tokenization, numericalization, language modeling, and word embeddings, are demonstrated in the module. The module is implemented in a guided fashion with the authors presenting the material and explaining concepts. Interactive features and extensive text commentary are provided directly in the notebook to facilitate self-guided learning and experimentation with the module. Keywords: Neural Networks, Negative Expression Recognition, Natural Language Processing, Computer Applications, Informatics © RSNA, 2021.

Full Text

Duke Authors

Cited Authors

  • Wiggins, WF; Kitamura, F; Santos, I; Prevedello, LM

Published Date

  • July 2021

Published In

Volume / Issue

  • 3 / 4

Start / End Page

  • e210035 -

PubMed ID

  • 34350414

Pubmed Central ID

  • PMC8328116

Electronic International Standard Serial Number (EISSN)

  • 2638-6100

Digital Object Identifier (DOI)

  • 10.1148/ryai.2021210035


  • eng

Conference Location

  • United States