@InProceedings{karimi-EtAl:2017:BioNLP17,
  author    = {Karimi, Sarvnaz  and  Dai, Xiang  and  Hassanzadeh, Hamedh  and  Nguyen, Anthony},
  title     = {Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {328--332},
  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.},
  url       = {http://www.aclweb.org/anthology/W17-2342}
}