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Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification.

Publication ,  Journal Article
Banerjee, I; Ling, Y; Chen, MC; Hasan, SA; Langlotz, CP; Moradzadeh, N; Chapman, B; Amrhein, T; Mong, D; Rubin, DL; Farri, O; Lungren, MP
Published in: Artif Intell Med
June 2019

This paper explores cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system - PEFinder and traditional machine learning methods - SVM and Adaboost. We proposed two distinct deep learning models - (i) CNN Word - Glove, and (ii) Domain phrase attention-based hierarchical recurrent neural network (DPA-HNN), for synthesizing information on pulmonary emboli (PE) from over 7370 clinical thoracic computed tomography (CT) free-text radiology reports collected from four major healthcare centers. Our proposed DPA-HNN model encodes domain-dependent phrases into an attention mechanism and represents a radiology report through a hierarchical RNN structure composed of word-level, sentence-level and document-level representations. Experimental results suggest that the performance of the deep learning models that are trained on a single institutional dataset, are better than rule-based PEFinder on our multi-institutional test sets. The best F1 score for the presence of PE in an adult patient population was 0.99 (DPA-HNN) and for a pediatrics population was 0.99 (HNN) which shows that the deep learning models being trained on adult data, demonstrated generalizability to pediatrics population with comparable accuracy. Our work suggests feasibility of broader usage of neural network models in automated classification of multi-institutional imaging text reports for a variety of applications including evaluation of imaging utilization, imaging yield, clinical decision support tools, and as part of automated classification of large corpus for medical imaging deep learning work.

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Published In

Artif Intell Med

DOI

EISSN

1873-2860

Publication Date

June 2019

Volume

97

Start / End Page

79 / 88

Location

Netherlands

Related Subject Headings

  • Radiography, Thoracic
  • Pulmonary Embolism
  • Neural Networks, Computer
  • Medical Informatics
  • Information Storage and Retrieval
  • Humans
  • Deep Learning
  • 46 Information and computing sciences
  • 42 Health sciences
  • 32 Biomedical and clinical sciences
 

Citation

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Banerjee, I., Ling, Y., Chen, M. C., Hasan, S. A., Langlotz, C. P., Moradzadeh, N., … Lungren, M. P. (2019). Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. Artif Intell Med, 97, 79–88. https://doi.org/10.1016/j.artmed.2018.11.004
Banerjee, Imon, Yuan Ling, Matthew C. Chen, Sadid A. Hasan, Curtis P. Langlotz, Nathaniel Moradzadeh, Brian Chapman, et al. “Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification.Artif Intell Med 97 (June 2019): 79–88. https://doi.org/10.1016/j.artmed.2018.11.004.
Banerjee I, Ling Y, Chen MC, Hasan SA, Langlotz CP, Moradzadeh N, et al. Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. Artif Intell Med. 2019 Jun;97:79–88.
Banerjee, Imon, et al. “Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification.Artif Intell Med, vol. 97, June 2019, pp. 79–88. Pubmed, doi:10.1016/j.artmed.2018.11.004.
Banerjee I, Ling Y, Chen MC, Hasan SA, Langlotz CP, Moradzadeh N, Chapman B, Amrhein T, Mong D, Rubin DL, Farri O, Lungren MP. Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. Artif Intell Med. 2019 Jun;97:79–88.
Journal cover image

Published In

Artif Intell Med

DOI

EISSN

1873-2860

Publication Date

June 2019

Volume

97

Start / End Page

79 / 88

Location

Netherlands

Related Subject Headings

  • Radiography, Thoracic
  • Pulmonary Embolism
  • Neural Networks, Computer
  • Medical Informatics
  • Information Storage and Retrieval
  • Humans
  • Deep Learning
  • 46 Information and computing sciences
  • 42 Health sciences
  • 32 Biomedical and clinical sciences