Cited By
View all- Ji SLi XSun WDong HTaalas AZhang YWu HPitkänen EMarttinen P(2024)A Unified Review of Deep Learning for Automated Medical CodingACM Computing Surveys10.1145/366461556:12(1-41)Online publication date: 17-May-2024
Towards Automatic ICD Coding via Knowledge Enhanced Multi-Task Learning
Pages 1238 - 1248
Abstract
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.
References
[1]
Emily Alsentzer, John R. Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew B. A. McDermott. 2019. Publicly Available Clinical BERT Embeddings. CoRR, Vol. abs/1904.03323 (2019).
[2]
Alan R. Aronson, Olivier Bodenreider, Dina Demner-Fushman, Kin Wah Fung, Vivian K. Lee, James G. Mork, Auré lie Né vé ol, Lee B. Peters, and Willie J. Rogers. 2007. From indexing the biomedical literature to coding clinical text: experience with MTI and machine learning approaches. In BioNLP@ACL. 105--112.
[3]
Alan R. Aronson and Francc ois-Michel Lang. 2010. An overview of MetaMap: historical perspective and recent advances. J. Am. Medical Informatics Assoc., Vol. 17, 3 (2010), 229--236.
[4]
Tal Baumel, Jumana Nassour-Kassis, Raphael Cohen, Michael Elhadad, and Noemie Elhadad. 2018. Multi-label classification of patient notes: case study on ICD code assignment. In AAAI Workshop.
[5]
Elena Birman-Deych, Amy D Waterman, Yan Yan, David S Nilasena, Martha J Radford, and Brian F Gage. 2005. Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors. Medical care (2005), 480--485.
[6]
Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Shengping Liu, and Weifeng Chong. 2020. HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding. In ACL. 3105--3114.
[7]
Shilei Cao, Buyue Qian, Changchang Yin, Xiaoyu Li, Jishang Wei, Qinghua Zheng, and Ian Davidson. 2017. Knowledge Guided Short-Text Classification for Healthcare Applications. In ICDM. 31--40.
[8]
Rich Caruana. 1997. Multitask Learning. Machine Learning, Vol. 28, 1 (1997), 41--75.
[9]
Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, and Wei Chu. 2020. Question Directed Graph Attention Network for Numerical Reasoning over Text. In EMNLP. 6759--6768.
[10]
Kyunghyun Cho, Bart van Merrienboer, cC aglar Gü lcc ehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In EMNLP. 1724--1734.
[11]
Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter F. Stewart. 2016. RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. In NIPS. 3504--3512.
[12]
Shaika Chowdhury, Chenwei Zhang, and Philip S. Yu. 2018. Multi-Task Pharmacovigilance Mining from Social Media Posts. In WWW.
[13]
Luciano R. S. de Lima, Alberto H. F. Laender, and Berthier A. Ribeiro-Neto. 1998. A Hierarchical Approach to the Automatic Categorization of Medical Documents. In CIKM. 132--139.
[14]
Paulina Grnarova, Florian Schmidt, Stephanie L. Hyland, and Carsten Eickhoff. 2016. Neural Document Embeddings for Intensive Care Patient Mortality Prediction. CoRR, Vol. abs/1612.00467 (2016).
[15]
William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS. 1024--1034.
[16]
Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016. MIMIC-III, a freely accessible critical care database. Scientific data, Vol. 3 (2016), 160035.
[17]
Ramakanth Kavuluru, Anthony Rios, and Yuan Lu. 2015. An empirical evaluation of supervised learning approaches in assigning diagnosis codes to electronic medical records. Artif. Intell. Medicine, Vol. 65, 2 (2015), 155--166.
[18]
Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. In EMNLP. 1746--1751.
[19]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR.
[20]
Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR.
[21]
Bevan Koopman, Guido Zuccon, Anthony N. Nguyen, Anton Bergheim, and Narelle Grayson. 2015. Automatic ICD-10 classification of cancers from free-text death certificates. Int. J. Medical Informatics, Vol. 84, 11 (2015), 956--965.
[22]
Hung Le, Truyen Tran, and Svetha Venkatesh. 2018. Dual Control Memory Augmented Neural Networks for Treatment Recommendations. In PAKDD. 273--284.
[23]
Fei Li and Hong Yu. 2020. ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network. In AAAI. 8180--8187.
[24]
Xinhang Li, Xiangyu Zhao, Jiaxing Xu, Yong Zhang, and Chunxiao Xing. 2023. IMF: Interactive Multimodal Fusion Model for Link Prediction. In WWW. 2572--2580.
[25]
Ning Liu, Pan Lu, Wei Zhang, and Jianyong Wang. 2019. Knowledge-Aware Deep Dual Networks for Text-Based Mortality Prediction. In ICDE. 1406--1417.
[26]
Ziru Liu, Jiejie Tian, Qingpeng Cai, Xiangyu Zhao, Jingtong Gao, Shuchang Liu, Dayou Chen, Tonghao He, Dong Zheng, Peng Jiang, and Kun Gai. 2023. Multi-Task Recommendations with Reinforcement Learning. In WWW. 1273--1282.
[27]
Junyu Luo, Cao Xiao, Lucas Glass, Jimeng Sun, and Fenglong Ma. 2021. Fusion: Towards Automated ICD Coding via Feature Compression. In Findings of ACL. 2096--2101.
[28]
Diego Marcheggiani, Joost Bastings, and Ivan Titov. 2018. Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks. In NAACL.
[29]
James Mullenbach, Sarah Wiegreffe, Jon Duke, Jimeng Sun, and Jacob Eisenstein. 2018. Explainable Prediction of Medical Codes from Clinical Text. In NAACL. 1101--1111.
[30]
Hao Peng, Jianxin Li, Yu He, Yaopeng Liu, Mengjiao Bao, Lihong Wang, Yangqiu Song, and Qiang Yang. 2018. Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN. In WWW. 1063--1072.
[31]
Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, and Wen-tau Yih. 2017. Cross-Sentence N-ary Relation Extraction with Graph LSTMs. TACL, Vol. 5 (2017), 101--115.
[32]
Adler J. Perotte, Rimma Pivovarov, Karthik Natarajan, Nicole Gray Weiskopf, Frank D. Wood, and Noemie Elhadad. 2014. Diagnosis code assignment: models and evaluation metrics. JAMIA, Vol. 21, 2 (2014), 231--237.
[33]
Aaditya Prakash, Siyuan Zhao, Sadid A. Hasan, Vivek V. Datla, Kathy Lee, Ashequl Qadir, Joey Liu, and Oladimeji Farri. 2017. Condensed Memory Networks for Clinical Diagnostic Inferencing. In AAAI. 3274--3280.
[34]
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2009. The Graph Neural Network Model. TNN, Vol. 20, 1 (2009), 61--80.
[35]
Henning Sch"a fer and Christoph M. Friedrich. 2019. UMLS mapping and Word embeddings for ICD code assignment using the MIMIC-III intensive care database. In EMBC. 6089--6092.
[36]
Elyne Scheurwegs, Boris Cule, Kim Luyckx, Lé on Luyten, and Walter Daelemans. 2017. Selecting relevant features from the electronic health record for clinical code prediction. J. Biomed. Informatics, Vol. 74 (2017), 92--103.
[37]
Junyuan Shang, Cao Xiao, Tengfei Ma, Hongyan Li, and Jimeng Sun. 2019. GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination. In AAAI. 1126--1133.
[38]
Haoran Shi, Pengtao Xie, Zhiting Hu, Ming Zhang, and Eric P. Xing. 2017. Towards Automated ICD Coding Using Deep Learning. CoRR, Vol. abs/1711.04075 (2017).
[39]
Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, and Eric P. Xing. 2020. Generalized Zero-Shot Text Classification for ICD Coding. In IJCAI. 4018--4024.
[40]
Wei Sun, Shaoxiong Ji, Erik Cambria, and Pekka Marttinen. 2021. Multitask Recalibrated Aggregation Network for Medical Code Prediction. CoRR, Vol. abs/2104.00952 (2021).
[41]
Bing Tian, Yong Zhang, Jin Wang, and Chunxiao Xing. 2019. Hierarchical Inter-Attention Network for Document Classification with Multi-Task Learning. In IJCAI. 3569--3575.
[42]
Shang-Chi Tsai, Chao-Wei Huang, and Yun-Nung Chen. 2021. Modeling Diagnostic Label Correlation for Automatic ICD Coding. In NAACL. 4043--4052.
[43]
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR.
[44]
Thanh Vu, Dat Quoc Nguyen, and Anthony Nguyen. 2020. A Label Attention Model for ICD Coding from Clinical Text. In IJCAI.
[45]
Yejing Wang, Zhaocheng Du, Xiangyu Zhao, Bo Chen, Huifeng Guo, Ruiming Tang, and Zhenhua Dong. 2023 a. Single-shot Feature Selection for Multi-task Recommendations. In SIGIR. 341--351.
[46]
Yejing Wang, Shen Ge, Xiangyu Zhao, Xian Wu, Tong Xu, Chen Ma, and Zhi Zheng. 2023 b. Doctor Specific Tag Recommendation for Online Medical Record Management. In KDD.
[47]
Rui Wu, Zhaopeng Qiu, Jiacheng Jiang, Guilin Qi, and Xian Wu. 2022. Conditional Generation Net for Medication Recommendation. In WWW. 935--945.
[48]
Cao Xiao, Edward Choi, and Jimeng Sun. 2018. Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. JAMIA, Vol. 25, 10 (2018), 1419--1428.
[49]
Xiancheng Xie, Yun Xiong, Philip S. Yu, and Yangyong Zhu. 2019. EHR with Multi-scale Feature Attention and Structured Knowledge Graph Propagation. In CIKM. 649--658.
[50]
Chaoqi Yang, Cao Xiao, Fenglong Ma, Lucas Glass, and Jimeng Sun. 2021b. SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations. In IJCAI. 3735--3741.
[51]
Tianchi Yang, Linmei Hu, Chuan Shi, Houye Ji, Xiaoli Li, and Liqiang Nie. 2021a. HGAT: Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification. TOIS, Vol. 39, 3 (2021), 32:1--32:29.
[52]
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alexander J. Smola, and Eduard H. Hovy. 2016. Hierarchical Attention Networks for Document Classification. In NAACL. 1480--1489.
[53]
Liang Yao, Chengsheng Mao, and Yuan Luo. 2019. Graph Convolutional Networks for Text Classification. In AAAI. 7370--7377.
[54]
Zheng Yuan, Chuanqi Tan, and Songfang Huang. 2022. Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding. In ACL. 808--814.
[55]
Tianzhu Zhang, Bernard Ghanem, Si Liu, and Narendra Ahuja. 2012. Robust visual tracking via multi-task sparse learning. In CVPR. 2042--2049.
[56]
Yutao Zhang, Robert Chen, Jie Tang, Walter F. Stewart, and Jimeng Sun. 2017. LEAP: Learning to Prescribe Effective and Safe Treatment Combinations for Multimorbidity. In SIGKDD. 1315--1324.
[57]
Yu Zhang and Qiang Yang. 2017. A Survey on Multi-Task Learning. CoRR, Vol. abs/1707.08114 (2017).
[58]
Zachariah Zhang, Jingshu Liu, and Narges Razavian. 2020. BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining. In ClinicalNLP@EMNLP. 24--34.
[59]
Zijian Zhang, Xiangyu Zhao, Hao Miao, Chunxu Zhang, Hongwei Zhao, and Junbo Zhang. 2023. AutoSTL: Automated Spatio-Temporal Multi-Task Learning. In AAAI. 4902--4910.
[60]
Zhi Zheng, Zhaopeng Qiu, Hui Xiong, Xian Wu, Tong Xu, Enhong Chen, and Xiangyu Zhao. 2022. DDR: Dialogue Based Doctor Recommendation for Online Medical Service. In KDD. 4592--4600.
[61]
Zhi Zheng, Chao Wang, Tong Xu, Dazhong Shen, Penggang Qin, Baoxing Huai, Tongzhu Liu, and Enhong Chen. 2021. Drug Package Recommendation via Interaction-aware Graph Induction. In WWW. 1284--1295.
[62]
Zhi Zheng, Chao Wang, Tong Xu, Dazhong Shen, Penggang Qin, Xiangyu Zhao, Baoxing Huai, Xian Wu, and Enhong Chen. 2023. Interaction-aware Drug Package Recommendation via Policy Gradient. TOIS, Vol. 41, 1 (2023), 3:1--3:32.
[63]
Tong Zhou, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Kun Niu, Weifeng Chong, and Shengping Liu. 2021. Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism. In ACL. 5948--5957.
Index Terms
- Towards Automatic ICD Coding via Knowledge Enhanced Multi-Task Learning
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October 2023
5508 pages
ISBN:9798400701245
DOI:10.1145/3583780
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View all- Ji SLi XSun WDong HTaalas AZhang YWu HPitkänen EMarttinen P(2024)A Unified Review of Deep Learning for Automated Medical CodingACM Computing Surveys10.1145/366461556:12(1-41)Online publication date: 17-May-2024
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