Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods
Sarvnaz Karimi, Xiang Dai, Hamed Hassanzadeh, Anthony Nguyen
Abstract
Diagnosis autocoding services and research intend to both improve the productivity of clinical coders and the accuracy of the coding. It is an important step in data analysis for funding and reimbursement, as well as health services planning and resource allocation. We investigate the applicability of deep learning at autocoding of radiology reports using International Classification of Diseases (ICD). Deep learning methods are known to require large training data. Our goal is to explore how to use these methods when the training data is sparse, skewed and relatively small, and how their effectiveness compares to conventional methods. We identify optimal parameters that could be used in setting up a convolutional neural network for autocoding with comparable results to that of conventional methods.- Anthology ID:
- W17-2342
- Volume:
- BioNLP 2017
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada,
- Editors:
- Kevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 328–332
- Language:
- URL:
- https://aclanthology.org/W17-2342
- DOI:
- 10.18653/v1/W17-2342
- Cite (ACL):
- Sarvnaz Karimi, Xiang Dai, Hamed Hassanzadeh, and Anthony Nguyen. 2017. Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods. In BioNLP 2017, pages 328–332, Vancouver, Canada,. Association for Computational Linguistics.
- Cite (Informal):
- Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods (Karimi et al., BioNLP 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/W17-2342.pdf
- Data
- IMDb Movie Reviews