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13th International Conference on Natural Language Processing and Chinese Computing

作者： Thang, Bin Wang, Junli Tongji Univ Minist Educ Key Lab Embedded Syst & Serv Comp Shanghai 201804 Peoples R China Tongji Univ Natl Prov Minist Joint Collaborat Innovat Ctr Fin Shanghai 201804 Peoples R China

ISBN: (纸本)9789819794362;9789819794379

icd(International Classification of Diseases) coding involves assigning icd codes to patients visit based on their medical notes. icd coding is a challenging multilabel text classification problem due to noisy medical document inputs. Recent advancements in automated icd coding have enhanced performance by integrating additional data and knowledge bases with the encoding of medical notes and codes. However, most of them ignore the code hierarchy, leading to improper code assignments. To address these problems, we propose a novel method based on associated and hierarchical code description distillation (AHDD) for better code representation learning and avoidance of improper code assignment. We utilize the code description and the hierarchical structure inherent to the icd codes. Therefore, in this paper, we leverage the code description and the hierarchical structure inherent to the icd codes. The code description is also applied to aware the attention layer and output layer. Experimental results on the benchmark dataset show the superiority of the proposed method over several state-of-the-art baselines.

关键词： icd coding associated code description hierarchical code description

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Automated icd coding via Contrastive Learning With Back-Reference and Synonym Knowledge for Smart Self-Diagnosis Applications

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第3期70卷 6042-6053页

作者： Zhao, Zhizhuo Lu, Wenpeng Peng, Xueping Xing, Lumin Zhang, Weiyu Zheng, Chaoqun Qilu Univ Technol Shandong Acad Sci Key Lab Comp Power Network & Informat Secur Minist EducShandong Comp Sci Ctr Jinan 250014 Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Comp Networks Jinan 250014 Peoples R China Univ Technol Sydney Australian Artificial Intelligence Inst Sydney NSW 2007 Australia Shandong First Med Univ Affiliated Hosp 1 Shandong Data Open Innovat Applicat Lab Jinan 250014 Peoples R China Shandong Prov Qianfoshan Hosp Jinan 250014 Peoples R China

Smart applications are essential in intelligent consumer electronics. With the rising focus on health concerns, self-diagnosis applications on smart devices have gained widespread popularity, attracting significant attention from both consumers and researchers. These applications play a crucial role in accurately predicting potential diseases based on user-provided symptom descriptions, thereby facilitating effective treatment. Distinguishing between diseases with similar symptoms yet requiring different treatments is essential. However, automated icd coding faces challenges due to subtle differences between some icd codes. Although existing methods has utilized contrastive learning to differentiate between codes, precise selection of high-quality negative samples and accurate matching of code descriptions with patient conditions remain critical factors influencing their performance. Drawing inspiration from medical practices, doctors tend to utilize back-reference knowledge to differentiate between similar diseases. We propose a novel framework for automated icd coding via contrastive learning with back-reference and synonym knowledge (BRSK) for smart self-diagnosis applications. Specifically, by incorporating back-reference knowledge, we generate challenging negative samples, enhancing the effectiveness of contrastive learning. Additionally, multi-view synonym matching networks are used to fully leverage synonym knowledge, resulting in more precise modeling of semantic matching between codes and patient descriptions. Extensive experiments on MIMIC-III datasets demonstrate that BRSK significantly outperformsstate-of-the-art baselines The source code and data are publicly available at https://***/zhaozhizhuo/icd-BRSK.

关键词： Codes Encoding Medical services Diseases Consumer electronics Contrastive learning Knowledge engineering icd coding self-diagnosis application contrastive learning back-reference knowledge multi-view synonym matching

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A Review on Deep Neural Networks for icd coding

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第5期35卷 4357-4375页

作者： Teng, Fei Liu, Yiming Li, Tianrui Zhang, Yi Li, Shuangqing Zhao, Yue Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 610032 Sichuan Peoples R China Sci & Technol Commun Secur Lab Chengdu 610041 Sichuan Peoples R China Sichuan Univ West China Hosp Chengdu 610041 Sichuan Peoples R China

The International Classification of Diseases (icd) is a standard for categorizing physical conditions, which has been widely used for analyzing clinical data and monitoring health issues. Manual icd coding takes a long time and is vulnerable to errors, so researchers pay more and more attention to the application of deep neural networks in icd automatic coding. However, there is still no comprehensive review of these studies and prospects for further research. This paper is not limited to the study of deep neural networks, but gives a formal definition of icd coding problems, and then systematically reviews the existing literature on how to design deep neural networks to address the four major challenges of icd coding tasks. This paper also summarizes the public data sets and future research directions, to provide guidance for the research of icd coding in medical field.

关键词： Encoding Codes Diseases Neural networks Deep learning Medical diagnostic imaging Taxonomy icd coding multi-label classification deep neural networks electronic health records model interpretability

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DRCNNTLe: A deep recurrent convolutional neural network with transfer learning through pre-trained embeddings for automated icd coding

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METHODS 2022年第0期205卷 97-105页

作者： Bhutto, Sajida Raz Wu, Yifan Zeng, Min Dogar, Abdul Wahab Ullah, Kaleem Li, Min Cent South Univ Sch Comp Sci & Engn Hunan Prov Key Lab Bioinformat Changsha 410083 Peoples R China Pir Abdul Qadir Shah Jeelani Inst Med Sci Dept Liver Transplant Gambat Sindh Pakistan

The International Classification of Diseases (icd), which is endorsed by the World Health Organization, is a diagnostic classification standard. icd codes store, retrieve, and analyze health information to make clinical decisions. Currently, icd coding has been adopted by more than 137 countries. However, in Pakistan, very few hospitals have implemented icd coding and conducted different epidemiological studies. Moreover, none of them have reported the spectrum of liver disease burden based on icd coding, nor implemented automated icd coding. In this study, we annotated icd codes for the database of the liver transplant unit of the Pir Abdul Qadir Shah Jeelani Institute of Medical Sciences. We named this database Medical Information Mart for Liver Transplantation (MIMLT). The results revealed that the database contains 34 icd codes, of which V70.8 is the most frequent code. Furthermore, we determined the spectrum of liver disease burden in liver recipients based on icd coding. We found that chronic hepatitis C (070.54) is the most frequent indication for liver transplantation. Additionally, we implemented automated icd coding utilizing the MIMLT database and proposed a novel Deep Recurrent Convolutional Neural Network with Transfer Learning through pre-trained Embeddings (DRCNNTLe) model, which is an extended version of our DRCNN-HP model. DRCNNTLe extracts robust text representations from its pre-trained embedding layer, which is trained on a large domain-specific MIMIC III database corpus. The results indicate that utilizing pre-trained word embeddings, which are trained on large domain-specific corpora can significantly improve the performance of the DRCNNTLe model and provide state-of-the-art results when the target database is small.

关键词： icd coding Clinical notes Liver disease Recurrent Convolutional Neural Network Transfer Learning Pre -trained Embeddings

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HieNet: Bidirectional Hierarchy Framework for Automated icd coding 1

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27th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Wang, Shi Tang, Daniel Zhang, Luchen Li, Huilin Han, Ding Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SNT TruX Luxembourg Luxembourg Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing Peoples R China Tech Univ Denmark Dept Civil Engn DK-2800 Lyngby Denmark Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (数字)9783031001260

ISBN: (纸本)9783031001260;9783031001253

International Classification of Diseases (icd) is a set of classification codes for medical records. Automated icd coding, which assigns unique International Classification of Diseases codes with each medical record, is widely used recently for its efficiency and error-prone avoidance. However, there are challenges that remain such as heterogeneity, label unbalance, and complex relationships between icd codes. In this work, we proposed a novel Bidirectional Hierarchy Framework(HieNet) to address the challenges. Specifically, a personalized PageRank routine is developed to capture the co-relation of codes, a bidirectional hierarchy passage encoder to capture the codes' hierarchical representations, and a progressive predicting method is then proposed to narrow down the semantic searching space of prediction. We validate our method on two widely used datasets. Experimental results on two authoritative public datasets demonstrate that our proposed method boosts the state-of-the-art performance by a large margin.

关键词： Structural encoder icd coding Bidirectional passage retriever Hierarchical embedding Healthcare Co-occurrence encoder

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Deep Learning for icd coding: Looking for Medical Concepts in Clinical Documents in English and in French 9th

Deep Learning for ICD Coding: Looking for Medical Concepts i...

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9th International Conference of the Conference-and-Labs-of-the-Evaluation-Forum-Association, (CLEF)

作者： Miftahutdinov, Zulfat Tutubalina, Elena Kazan Volga Reg Fed Univ Kazan Russia Neuromation OU EE-10111 Tallinn Estonia

ISBN: (纸本)9783319989327;9783319989310

Medical Concept coding (MCD) is a crucial task in biomedical information extraction. Recent advances in neural network modeling have demonstrated its usefulness in the task of natural language processing. Modern framework of sequence-to-sequence learning that was initially used for recurrent neural networks has been shown to provide powerful solution to tasks such as Named Entity Recognition or Medical Concept coding. We have addressed the identification of clinical concepts within the International Classification of Diseases version 10 (icd-10) in two benchmark data sets of death certificates provided for the task 1 in the CLEF eHealth shared task 2017. A proposed architecture combines ideas from recurrent neural networks and traditional text retrieval term weighting schemes. We found that our models reach accuracy of 75% and 86% as evaluated by the F-measure on the CepiDc corpus of French texts and on the CDC corpus of English texts, respectfully. The proposed models can be employed for coding electronic medical records with icd codes including diagnosis and procedure codes.

关键词： icd coding icd codes Medical concept coding Medical record coding Computer assisted coding Recurrent neural network Encoder-decoder model Deep learning Machine learning Death certificates CepiDc corpus CDC corpus Healthcare CLEF eHealth

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MULTI-LABEL CLASSIFICATION OF icd coding USING DEEP LEARNING

MULTI-LABEL CLASSIFICATION OF ICD CODING USING DEEP LEARNING

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International Symposium on Community-Centric Systems (CcS)

作者： Hsu, Chung-Chian Chang, Pei-Chi Chang, Arthur Natl Yunlin Univ Sci & Technol Dept Informat Management Touliu Yunlin Taiwan Natl Yunlin Univ Sci & Technol Bachelor Program Interdisciplinary Studies Touliu Yunlin Taiwan

ISBN: (纸本)9781728187419

This study uses deep learning approach to tackle the multi-label classification problem in icd coding. The discharge summaries on MIMIC-III dataset are adopted to explore the training methods of text preprocessing, label preprocessing, and model training. Specifically, the methods of label-to-chapter and common label classification are experimented, hoping to find the best recommendations of icd codes for each diagnosis by the physicians. The result shows CNN has the best performance 76% by micro F1-measure in label-to-chapter. In addition, it also shows CNN outperforms other methods within top-50 and top-100 common labels.

关键词： icd coding multi-label classification natural language processing deep learning

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Towards Automatic icd coding via Knowledge Enhanced Multi-Task Learning 23

Towards Automatic ICD Coding via Knowledge Enhanced Multi-Ta...

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32nd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Li, Xinhang Zhao, Xiangyu Zhang, Yong Xing, Chunxiao Tsinghua Univ Dept Comp Sci & Techonol Beijing Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

ISBN: (纸本)9798400701245

The aim of icd coding is to assign International Classification of Diseases (icd) codes to unstructured clinical notes or discharge summaries. Numerous methods have been proposed for automatic icd coding in an effort to reduce human labor and errors. However, existing works disregard the data imbalance problem of clinical notes. In addition, the noisy clinical note issue has not been thoroughly investigated. To address such issues, we propose a knowledge enhanced Graph Attention Network (GAT) under multi-task learning setting. Specifically, multi-level information transitions and interactions have been implemented. On the one hand, a large heterogeneous text graph is constructed to capture both intra- and inter-note correlations between various semantic concepts, thereby alleviating the data imbalance issue. On the other hand, two auxiliary healthcare tasks have been proposed to facilitate the sharing of information across tasks. Moreover, to tackle the issue of noisy clinical notes, we propose to utilize the rich structured knowledge facts and information provided by medical domain knowledge, thereby encouraging the model to focus on the clinical notes' noteworthy portion and valuable information. The experimental results on the widely-used medical dataset, MIMIC-III, demonstrate the advantages of our proposed framework.

关键词： icd coding multi-task learning knowledge graph

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Embed Wisely: An Ensemble Approach to Predict icd coding 21st

Embed Wisely: An Ensemble Approach to Predict ICD Coding

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21st Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Rajendran, Pavithra Zenonos, Alexandros Spear, Joshua Pope, Rebecca KPMG UK 15 Canada Sq London E14 5GL England Great Ormond St Hosp Children NHS Fdn Trust Great Ormond St London WC1N 3JH England UCL Great Ormond St Inst Child Hlth 30 Guilford St London WC1N 1EH England

ISBN: (纸本)9783030937331;9783030937324

International Classification of Diseases (icd) are the de facto codes used globally for clinical coding. These codes enable healthcare providers to claim reimbursement and facilitate efficient storage and retrieval of diagnostic information. The problem of automatically assigning icd codes has been previously approached as a multilabel classification problem, using neural models and unstructured data. We utilise an approach for efficiently combining multiple sets of pretrained word embeddings to enhance the performance on icd code prediction. Using post-processing and meta-embeddings techniques, we exploit the geometric properties of word embeddings and combine different sets of word embeddings into a common dimensional space. We empirically show that infusing information from biomedical articles, whilst preserving the local neighbourhood of the embedding, improves the current state-of-the-art deep learning architectures. Furthermore, we demonstrate the efficacy of this approach for a multi-modal setting, using unstructured and structured information.

关键词： icd coding Embeddings Multimodal

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MHLAT: Multi-Hop Label-Wise Attention Model for Automatic icd coding 48

MHLAT: Multi-Hop Label-Wise Attention Model for Automatic IC...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Duan, Junwen Jiang, Han Yu, Ying Central South University School of Computer Science and Engineering Hunan Changsha China Central South University Hunan Provincial Key Lab on Bioinformatics Hunan Changsha China

ISBN: (纸本)9781728163277

International Classification of Diseases (icd) coding is the task of assigning icd diagnosis codes to clinical notes. This can be challenging given the large quantity of labels (nearly 9,000) and lengthy texts (up to 8,000 tokens). However, unlike the single-pass reading process in previous works, humans tend to read the text and label definitions again to get more confident answers. Moreover, although pretrained language models have been used to address these problems, they suffer from huge memory usage. To address the above problems, we propose a simple but effective model called the Multi-Hop Label-wise ATtention (MHLAT), in which multi-hop label-wise attention is deployed to get more precise and informative representations. Extensive experiments on three benchmark MIMIC datasets indicate that our method achieves significantly better or competitive performance on all seven metrics, with much fewer parameters to optimize. © 2023 IEEE.

关键词： bias-terms fine-tuning icd coding multi-hop label-wise attention pre-trained language model

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