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-ofthe-art methods.

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 cooccurrence, consequently alleviating the longtail 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.

We propose a new method that balances attribute coverage for short-length cognitive diagnostic computerized adaptive testing (CD-CAT). The new method uses the attribute discrimination index (ADI-based method) instead of the number of items that measure each attribute [modified global discrimination index (MGDI)-based method] to balance the attribute coverage. Therefore, the information that each attribute provides can be captured. The purpose of the simulation study was to evaluate the performance of the new method, and the results showed the following: (a) Compared with uncontrolled attribute-balance coverage method, the new method produced a higher mastery pattern correct classification rate (PCCR) and attribute correct classification rate (ACCR) with both the posterior-weighted Kullback-Leibler (PWKL) and the modified PWKL (MPWKL) item selection method. (b) Equalization of ACCR (E-ACCR) based on the ADI-based method leads to better results, followed by the MGDI-based method. The uncontrolled method leads to the worst results regardless of item selection methods. (c) Both the ADI-based and MGDI-based methods produced acceptable examinee qualification rates, regardless of item selection methods, although they were relatively low for the uncontrolled condition.Keywords: balance attribute coverage, cognitive diagnostic computerized adaptive testing, attribute discrimination index, equalization of attribute correct classification rate, examinee qualification rate Frontiers in Psychology | www.frontiersin.org

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.

This is the system description of the CA-SIA_Unisound team for Task 1, Task 7b, and Task 8 of the sixth Social Media Mining for Health Applications (SMM4H) shared task in 2021. To address two shared challenges among those tasks, the colloquial text and the imbalance annotation, we apply customized pre-trained language models 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.