Weifeng Chong


2021

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Classification, Extraction, and Normalization : CASIA_Unisound Team at the Social Media Mining for Health 2021 Shared Tasks
Tong Zhou | Zhucong Li | Zhen Gan | Baoli Zhang | Yubo Chen | Kun Niu | Jing Wan | Kang Liu | Jun Zhao | Yafei Shi | Weifeng Chong | Shengping Liu
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task

This is the system description of the CASIA_Unisound team for Task 1, Task 7b, and Task 8 of the sixth Social Media Mining for Health Applications (SMM4H) shared task in 2021. Targeting on deal with two shared challenges, the colloquial text and the imbalance annotation, among those tasks, we apply a customized pre-trained language model and propose various training strategies. Experimental results show the effectiveness of our system. Moreover, we got an F1-score of 0.87 in task 8, which is the highest among all participates.

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Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism
Tong Zhou | Pengfei Cao | Yubo Chen | Kang Liu | Jun Zhao | Kun Niu | Weifeng Chong | Shengping Liu
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

The ICD coding task aims at assigning codes of the International Classification of Diseases in clinical notes. Since manual coding is very laborious and prone to errors, many methods have been proposed for the automatic ICD coding task. However, existing works either ignore the long-tail of code frequency or the noisy clinical notes. To address the above issues, we propose an Interactive Shared Representation Network with Self-Distillation Mechanism. Specifically, an interactive shared representation network targets building connections among codes while modeling the co-occurrence, consequently alleviating the long-tail problem. Moreover, to cope with the noisy text issue, we encourage the model to focus on the clinical note’s noteworthy part and extract valuable information through a self-distillation learning mechanism. Experimental results on two MIMIC datasets demonstrate the effectiveness of our method.

2020

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HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding
Pengfei Cao | Yubo Chen | Kang Liu | Jun Zhao | Shengping Liu | Weifeng Chong
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The International Classification of Diseases (ICD) provides a standardized way for classifying diseases, which endows each disease with a unique code. ICD coding aims to assign proper ICD codes to a medical record. Since manual coding is very laborious and prone to errors, many methods have been proposed for the automatic ICD coding task. However, most of existing methods independently predict each code, ignoring two important characteristics: Code Hierarchy and Code Co-occurrence. In this paper, we propose a Hyperbolic and Co-graph Representation method (HyperCore) to address the above problem. Specifically, we propose a hyperbolic representation method to leverage the code hierarchy. Moreover, we propose a graph convolutional network to utilize the code co-occurrence. Experimental results on two widely used datasets demonstrate that our proposed model outperforms previous state-of-the-art methods.

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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
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

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.