Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods

By Sarvnaz Karimi, Xiang Dai, Hamed Hassanzadeh, Anthony N. Nguyen. 2017

64 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Revisiting Transformer-based Models for Long Document Classification (2022, methodology): “However, both report that Transformers-based models perform worse than CNN-based ones.”
Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, methodology): “Radiology reports using the International Coding of Diseases (ICD) were auto-encoded in [55] using a publicly available dataset.”