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13th International Conference on Natural Language Processing and Chinese Computing

作者： Shen, Hualei Wang, Haizhu Shangguan, Guoqing Li, Dong Henan Normal Univ Coll Comp & Informat Engn Xinxiang Henan Peoples R China Key Lab Artificial Intelligence & Personalized Le Xinxiang Henan Peoples R China Big Data Engn Lab Teaching Resources & Assessment Xinxiang Henan Peoples R China

ISBN: (纸本)9789819794393;9789819794409

automatically assigning multiple International Classification of Diseases (icd) codes to a clinical note is challenging due to the unstructured and verbose nature of medical records. Currently, most methods employ deep neural networks to learn the representation of clinical notes from a single perspective. These single-view-based methods overlook the exploitation and fusion of multiview features to enhance the precision of icd coding. In this paper, we propose a new Multiview Attention Network (MANet) to extract and fuse multiview features for icd coding. MANet includes a specially designed multiview attention scheme to extract and fuse coarse, global, and local features from clinical notes. Additionally, a novel cascaded multilayer perceptron (MLP) block and a multiscale convolution block are designed to extract global and local features, respectively. Self-attention and cross-attention are integrated to effectively fuse these multiview features, generating more informative and discriminative representations. Extensive experiments conducted on the popular MIMIC-III and MIMIC-IV-icd9 datasets demonstrate the superiority of our proposed MANet over state-of-the-art methods. On MIMIC-III, MANet achieves a Macro-AUC of 0.953, Micro-AUC of 0.993, Macro-F1 of 0.135, Micro-F1 of 0.596, precision at top 8 (P@8) of 0.773, and precision at top 15 (P@15) of 0.617. On MIMIC-IV-icd9, MANet achieves a Macro-AUC of 0.968, Micro-AUC of 0.996, Macro-F1 of 0.146, Micro-F1 of 0.614, P@8 of 0.698, and P@15 of 0.526.

关键词： automatic icd coding Multiview features fusion Multiview attention

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HiRes: Hierarchical Feature Optimization and Rescorer for automatic icd coding

HiRes: Hierarchical Feature Optimization and Rescorer for Au...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liang, Zhenpeng Guan, Hongjiao Lu, Wenpeng Peng, Xueping Xu, Bing Yang, Muyun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350368741

The International Classification of Diseases (icd) coding assigns standardized codes to diseases. Automating this process enhances the efficiency and accuracy of clinical records processing. However, current methods struggle with noisy and lengthy clinical texts, making it difficult to ensure the reliability of feature extraction. Furthermore, they typically make separate binary predictions for each code, overlooking the dependencies between them. To address these issues, we propose a novel model called Hierarchical Feature Optimization and Rescorer (HiRes). We employ a cascaded convolution architecture to mitigate noise and enhance feature representation. Additionally, a masked autoencoder-based rescorer is introduced to capture icd code interdependencies, refining initial predictions for improved accuracy. The model also considers the inconsistencies in code representations. Experiments on the MIMIC datasets demonstrate the effectiveness of our model. © 2025 IEEE.

关键词： automatic icd coding cascaded convolutional network multi-label classification

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KAicd: A knowledge attention-based deep learning framework for automatic icd coding

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NEUROCOMPUTING 2022年第0期469卷 376-383页

作者： Wu, Yifan Zeng, Min Fei, Zhihui Yu, Ying Wu, Fang-Xiang Li, Min Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Univ Saskatchewan Div Biomed Engn & Dept Mech Engn Saskatoon SK SKS7 N5A9 Canada

automatic International Classification of Diseases (icd) coding is an important task in the future of arti-ficial intelligence healthcare. In recent years, a lot of traditional machine learning-based methods have been proposed, and they achieved good results on this task. However, these traditional machine learning-based methods for automatic icd coding only focus on the semantic features of clinical notes and ignore the feature extraction of icd titles that are the descriptions of icd codes. In this paper, we pro -pose a knowledge attention-based deep learning framework called KAicd for automatic icd coding. KAicd makes full use of the clinic notes and the icd titles. The semantic features of clinic notes are extracted by a multi-scale convolutional neural network. For icd titles, we use attention-based Bidirectional Gated Recurrent Unit (Bi-GRU) to build a knowledge database, which can offer additional information. Depending on input clinic notes, we can use the attention mechanism to obtain different knowledge vectors from the knowledge database where some icd titles are more relevant to the input clinic notes. Last, we concatenate the knowledge vectors and the semantic features of clinic notes, and use them for the final prediction. KAicd is tested on a public dataset Medical Information Mart for Intensive Care III (MIMIC III);it achieves micro-precision of 0.502, micro-recall of 0.428, and micro-f1 of 0.462, which outperforms other competing methods. Furthermore, the results of the ablation study show that the knowledge database of icd titles learned by the attention-based Bi-GRU enhances the fea-ture expression and improves the prediction performance. (c) 2020 Published by Elsevier B.V.

关键词： automatic icd coding Clinic notes icd titles Bidirectional Gated Recurrent Unit Attention Knowledge database

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A Pseudo Label-Wise Attention Network for automatic icd coding

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第10期26卷 5201-5212页

作者： Wu, Yifan Zeng, Min Yu, Ying Li, Yaohang Li, Min Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Old Dominion Univ Dept Comp Sci Norfolk VA 23529 USA

automatic International Classification of Diseases (icd) coding is defined as a kind of text multi-label classification problem, which is difficult because the number of labels is very large and the distribution of labels is unbalanced. The label-wise attention mechanism is widely used in automatic icd coding because it can assign weights to every word in full Electronic Medical Records (EMR) for different icd codes. However, the label-wise attention mechanism is redundant and costly in computing. In this paper, we propose a pseudo label-wise attention mechanism to tackle the problem. Instead of computing different attention modes for different icd codes, the pseudo label-wise attention mechanism automatically merges similar icd codes and computes only one attention mode for the similar icd codes, which greatly compresses the number of attention modes and improves the predicted accuracy. In addition, we apply a more convenient and effective way to obtain the icd vectors, and thus our model can predict new icd codes by calculating the similarities between EMR vectors and icd vectors. Our model demonstrates effectiveness in extensive computational experiments. On the public MIMIC-III dataset and private Xiangya dataset, our model achieves the best performance on micro F1 (0.583 and 0.806), micro AUC (0.986 and 0.994), P@8 (0.756 and 0.413), and costs much smaller GPU memory (about 26.1% of the models with label-wise attention). Furthermore, we verify the ability of our model in predicting new icd codes. The interpretablility analysis and case study show the effectiveness and reliability of the patterns obtained by the pseudo label-wise attention mechanism.

关键词： Codes Encoding Predictive models Computational modeling Learning systems Feature extraction Semantics automatic icd coding pseudo label-wise attention text multi-label classification multi-label learning

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JAN: Joint Attention Networks for automatic icd coding

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第10期26卷 5235-5246页

作者： Wu, Yuzhou Chen, Zhigang Yao, Xin Chen, Xuechen Zhou, Zeren Xue, Jinkai Cent South Univ Sch Comp Sci & Engn Changsha 410012 Peoples R China

The International Classification of Diseases (icd) code is a disease classification method formulated by the World Health Organization(WHO). icd coding usually requires clinicians to manually allocate icd codes to clinical documents, which is labor-intensive, expensive, and error-prone. Therefore, many methods have been introduced for automatic icd coding. However, most of the methods have ignored or cannot combine two essential features well: long-tailed label distribution and label correlation. In this paper, we propose a novel end-to-end Joint Attention Network (JAN) to solve these two problems. JAN includes Document-based attention and Label-based attention to capture semantic information from clinical document text and label description, respectively, which helps solve the classification of dense and sparse data in long-tailed label distribution. Besides, an Adaptive fusion layer and CorNet block are presented to adaptively adjust the weight of these two attentions and exploit label co-occurrence relations, respectively. Experiments on the MIMIC-III and MIMIC-II datasets demonstrate that our proposed JAN outperformed previous state-of-art methods achieving Micro-F1 of 0.553, Micro-AUC of 0.989 and precision at top 8(P@8) of 0.735. Finally, we also provide attention and label correlation visualization to verify the effectiveness of our model and improve the interpretation of our deep learning-based method.

关键词： Codes Encoding Correlation Diseases Semantics MIMICs Computer science automatic icd coding attention network long-tailed label distribution label correlation

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A Large-Scale Hierarchical Structure Knowledge Enhanced Pre-training Framework for automatic icd coding 28th

A Large-Scale Hierarchical Structure Knowledge Enhanced Pre-...

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28th International Conference on Neural Information Processing

作者： Wang, Shi Tang, Daniel Zhang, Luchen Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China

ISBN: (纸本)9783030923099;9783030923105

icd coding is usually considered as a multi-label prediction task, which assigns accurate multiple icd codes to clinical texts. In this paper, we present a novel way of injecting prior knowledge of hierarchical structures into BERT (HieBERT) to predict icd codes automatically. Hierarchical structures consist of code tree positions and code tree sequence LSTM embeddings. We generate them as hierarchical representations of icd codes. Besides, we train a clinical BERT model on millions of clinical texts to capture contextual and co-occurrence information. Then, we propose an aligning method to map the hierarchical representations into BERT vector space. We implement HieBERT on a widely used dataset: MIMIC-III. And the experimental results indicate that our proposed model achieves great performance compared with previous works.

关键词： Structural and co-occurrence embedding automatic icd coding Hierarchical BERT

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MKFN: Multimodal Knowledge Fusion Network for automatic icd coding

MKFN: Multimodal Knowledge Fusion Network for Automatic ICD ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Shilong Lin, Hongfei Zhang, Yijia Li, Xiaobo Qu, Wen Dalian Maritime University School of Information Science and Technology Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798350337488

Automated International Classification of Diseases (icd) coding tasks are designed to assign diagnosis and procedure codes to patients' electronic medical records (EMRs). Recent works have applied deep neural network models and related techniques for code assignment to clinical notes. However, most existing methods have overlooked the advantageous complementary information presented in the tabular data from EMRs and the Wikipedia knowledge. Therefore, we propose a Multimodal Knowledge Fusion Network (MKFN) to effectively integrate clinical notes, tabular data, and Wikipedia knowledge, enhancing the model's predictive capabilities. We incorporate structured tabular data and clinical notes into an initial multimodal representation using label attention and self-attention mechanisms. We propose a knowledge fusion network to leverage tabular data and Wikipedia knowledge for accurate predictions when code descriptions are absent in clinical notes. Experiments on the MIMIC dataset show that our proposed model achieves competitive results among existing icd coding methods. © 2023 IEEE.

关键词： automatic icd coding joint learning medical knowledge fusion

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DILM-icd: A Deep Iterative Learning Model for automatic icd coding

DILM-ICD: A Deep Iterative Learning Model for Automatic ICD ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qiu, Weiyan Wu, Yifan Li, Yiming Niu, Kunying Zeng, Min Li, Min Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350337488

automatic International Classification of Disease (icd) coding plays a crucial role in assigning icd codes to electronic medical records. This task presents a challenging multi-label text classification problem due to the vast number of icd codes and the imbalanced label distribution. However, accurately predicting all labels simultaneously is extremely difficult for such the large label space. In this paper, we propose a novel model called Deep Iterative Learning Model (DILM-icd), which uses an iterative learning framework to perform automatic icd coding task. The iterative learning framework can refine the prediction results by repeating the iteration modules, which simulates the human-like coding process. In addition, we propose a multi-head text-label matching mechanism, which combines the embedding icd description information to better match the relationship between text and label. The combination of the iterative learning framework with the multi-head text-label matching mechanism enables the model pay attention to lowfrequency icd codes. DILM-icd is evaluated on the MIMIC-III-full dataset and MIMIC-III-50 dataset. The experimental results show that DILM-icd achieves state-of-the-art results across multiple evaluation metrics, which demonstrates the effectiveness of our proposed model. © 2023 IEEE.

关键词： automatic icd coding iterative learning multi-label text classification

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Modelling long medical documents and code associations for explainable automatic icd coding

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期249卷

作者： Hou, Wen-hui Wang, Xiao-kang Wang, Ya-nan Wang, Jian-qiang Xiao, Fei Cent South Univ Sch Business Changsha 410083 Peoples R China Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China

Quick and accurate International Classification of Diseases (icd) code assignment is vital for billing, reimbursement and medical research. Owing to the labour-intensive and error-prone nature of manual coding, automatic icd coding using deep learning methods has flourished. However, this task is still challenging because of (1) the interpretability of coding, (2) lengthy clinical documents and (3) long-tail label distribution. In the current study, we propose a novel automatic icd coding framework to address these issues. First, a biomedicalspecific pre-trained language model, Clinical-Longformer, is used as an encoder, which generates meaningful representations of long clinical documents by injecting rich medical knowledge and capturing long-distance dependence among tokens. Second, a decoding architecture that combines the multi-synonym attention mechanism, hierarchical curriculum learning and distribution-balance loss is designed to perform icd code prediction. The decoder improves the tail-end performance by fully capturing code associations in terms of semantics, structure and co-occurrence. In addition, the label-wise attention mechanism provides the interpretability of prediction. Experimental results on benchmark MIMIC-III datasets indicate that our model achieves higher F1 scores than previous state-of-the-art baselines. Our suggested model is expected to serve as an aid in improving the efficiency of manual icd coding and to offer insights for other long text classification tasks with multiple label associations.

关键词： automatic icd coding Multi -label text classification Long medical documents Code associations Interpretability

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automatic icd-10-CM coding via Lambda-Scaled attention based deep learning model

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METHODS 2024年 222卷 19-27页

作者： Bhutto, Sajida Raz Zeng, Min Niu, Kunying Khoso, Sirajuddin Umar, Muhammad Lalley, Gul Li, Min Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China MUET Dept Software Engn SZAB Campus Khairpur Mirs Pakistan Pir Abdul Qadir Shah Jeelani Inst Med Sci Dept Liver Transplant Gambat Sindh Pakistan

The International Classification of Diseases (icd) serves as a global healthcare administration standard, with one of its editions being icd-10-CM, an enhanced diagnostic classification system featuring numerous new codes for specific anatomic sites, co -morbidities, and causes. These additions facilitate conveying the complexities of various diseases. Currently, icd-10 coding is widely adopted worldwide. However, public hospitals in Pakistan have yet to implement it and automate the coding process. In this research, we implemented icd-10-CM coding for a private database and named it Clinical Pool of Liver Transplant (CPLT). Additionally, we proposed a novel deep learning model called Deep Recurrent -Convolution Neural Network with a lambda -scaled Attention module (DRCNN-ATT) using the CPLT database to achieve automatic icd-10-CM coding. DRCNN-ATT combines a bidirectional long short-term memory network (bi-LSTM), a multi -scale convolutional neural network (MSCNN), and a lambda -scaled attention module. Experimental results demonstrate that deep recurrent convolutional neural network (DRCNN) faces attention score vanishing problem with a standard attention module for automatic icd coding. However, adding a lambda -scaled attention module resolves this issue. We evaluated DRCNN-ATT model using two distinct datasets: a private CPLT dataset and a public MIMIC III top 50 dataset. The results indicate that the DRCNN-ATT model outperformed various baselines by generating 0.862 micro F1 and 0.25 macro F1 scores on CPLT dataset and 0.705 micro F1 and 0.655 macro F1 scores on MIMIC III top 50 dataset. Furthermore, we also deployed our model for automatic icd-10-CM coding using ngrok and the Flask APIs, which receives input, processes it, and then returns the results.

关键词： Medical Clinical notes automatic icd coding Deep learning techniques Attention mechanism Flask API

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