Clinical-Coder: Assigning Interpretable ICD-10 Codes to Chinese Clinical Notes
Pengfei Cao, Chenwei Yan, Xiangling Fu, Yubo Chen, Kang Liu, Jun Zhao, Shengping Liu, Weifeng Chong
System Demonstrations Demo Paper
Demo Session 1B-3: Jul 8
(05:45-06:45 GMT)
Demo Session 3B-3: Jul 8
(12:45-13:45 GMT)
Abstract:
In this paper, we introduce Clinical-Coder, an online system aiming to assign ICD codes to Chinese clinical notes. ICD coding has been a research hotspot of clinical medicine, but the interpretability of prediction hinders its practical application. We exploit a Dilated Convolutional Attention network with N-gram Matching mechanism (DCANM) to capture semantic features for non-continuous words and continuous n-gram words, concentrating on explaining the reason why each ICD code to be predicted. The experiments demonstrate that our approach is effective and that our system is able to provide supporting information in clinical decision making.
You can open the
pre-recorded video
in a separate window.
NOTE: The SlidesLive video may display a random order of the authors.
The correct author list is shown at the top of this webpage.
Similar Papers
Rationalizing Medical Relation Prediction from Corpus-level Statistics
Zhen Wang, Jennifer Lee, Simon Lin, Huan Sun,

HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding
Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Shengping Liu, Weifeng Chong,

BENTO: A Visual Platform for Building Clinical NLP Pipelines Based on CodaLab
Yonghao Jin, Fei Li, Hong Yu,

Learning to Deceive with Attention-Based Explanations
Danish Pruthi, Mansi Gupta, Bhuwan Dhingra, Graham Neubig, Zachary C. Lipton,
