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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79824完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 鄭卜壬(Pu-Jen Cheng) | |
| dc.contributor.author | Min Huang | en |
| dc.contributor.author | 黃敏 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:12:46Z | - |
| dc.date.available | 2023-12-31 | |
| dc.date.available | 2022-11-23T09:12:46Z | - |
| dc.date.copyright | 2021-08-20 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-16 | |
| dc.identifier.citation | [1] Xuedong Li, Yue Wang, Dongwu Wang, Walter Yuan, Dezhong Peng, and Qiaozhu Mei. Improving rare disease classification using imperfect knowledge graph. BMC medical informatics and decision making, 19(5):1–10, 2019. [2] Mohammed Alawad, Shang Gao, Mayanka Chandra Shekar, SM Hasan, J Blair Christian, XiaoCheng Wu, Eric B Durbin, Jennifer Doherty, Antoinette Stroup, Linda Coyle, et al. Integration of domain knowledge using medical knowledge graph deep learning for cancer phenotyping. arXiv preprint arXiv:2101.01337, 2021. [3] Leah S Larkey and W Bruce Croft. Combining classifiers in text categorization. In Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, pages 289–297, 1996. [4] Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman LiWei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. Mimiciii, a freely accessible critical care database. Scientific data, 3(1):1–9, 2016. [5] James Mullenbach, Sarah Wiegreffe, Jon Duke, Jimeng Sun, and Jacob Eisenstein. Explainable prediction of medical codes from clinical text. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1101–1111, 2018. [6] Fei Li and Hong Yu. Icd coding from clinical text using multifilter residual convolutional neural network. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 8180–8187, 2020. [7] Thanh Vu, Dat Quoc Nguyen, and Anthony Nguyen. A label attention model for icd coding from clinical text. [8] Tian Bai and Slobodan Vucetic. Improving medical code prediction from clinical text via incorporating online knowledge sources. In The World Wide Web Conference, pages 72–82, 2019. [9] Xiancheng Xie, Yun Xiong, Philip S Yu, and Yangyong Zhu. Ehr coding with multiscale feature attention and structured knowledge graph propagation. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pages 649–658, 2019. [10] Fei Teng, Wei Yang, Li Chen, LuFei Huang, and Qiang Xu. Explainable prediction of medical codes with knowledge graphs. Frontiers in Bioengineering and Biotechnology, 8:867, 2020. [11] Antoine Bordes, Nicolas Usunier, Alberto GarciaDuran, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multirelational data. In Neural Information Processing Systems (NIPS), pages 1–9, 2013. [12] Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 29, 2015. [13] Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. Knowledge graph embedding via dynamic mapping matrix. In Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: Long papers), pages 687–696, 2015. [14] Zhiqing Sun, ZhiHong Deng, JianYun Nie, and Jian Tang. Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197, 2019. [15] Olivier Bodenreider. The unified medical language system (umls): integrating biomedical terminology. Nucleic acids research, 32(suppl_1):D267–D270, 2004. [16] Luca Soldaini and Nazli Goharian. Quickumls: a fast, unsupervised approach for medical concept extraction. In MedIR workshop, sigir, pages 1–4, 2016. [17] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013. [18] Xu Han, Shulin Cao, Xin Lv, Yankai Lin, Zhiyuan Liu, Maosong Sun, and Juanzi Li. Openke: An open toolkit for knowledge embedding. In Proceedings of the 2018 conference on empirical methods in natural language processing: system demonstrations, pages 139–144, 2018. [19] Daixin Wang, Peng Cui, and Wenwu Zhu. Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1225–1234, 2016. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79824 | - |
| dc.description.abstract | 醫療編碼指為醫學敘述加上編碼，用以表示醫療診斷和處置。它的好處在於將自由形式的文字標準化，可應用於：健康追蹤、醫療決策、統計分析、保險費用估價等。其中，最常用的國際編碼以國際疾病統計分類(ICD)為大宗。現今在醫院內，有疾病分類師為病歷標上國際疾病統計分類；然而，國際疾病統計分類的類別眾多，即使是專業人員也不一定能正確標記。隨著電子病歷的普及和預測模型的發展，自動化國際疾病統計分類成為一個長遠的研究目標。 在這篇論文中，我們提出了一個方法結合醫學知識圖譜以幫助國際疾病統計分類。我們的核心想法是：病歷中重要的醫學文字應對分類結果造成較大的影響。我們提取病歷中的醫學概念，透過計算概念和國際疾病統計分類的相似度，藉此提高重要醫學文字在注意力機制中的權重。在知識圖譜的幫助下，實驗顯示我們提出的方法能幫助先前的模型更好地預測國際疾病統計分類。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:12:46Z (GMT). No. of bitstreams: 1 U0001-0508202119215700.pdf: 1443002 bytes, checksum: a4b454b24e9034863413ca0ee247618f (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 致謝 i 摘要 ii Abstract iii Contents iv List of Figures vii List of Tables viii Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 2 1.3 Challenges and Main Contributions 3 Chapter 2 Related Work 5 2.1 Automatic ICD Coding 5 2.2 Knowledge Based ICD Coding Model 5 2.3 Attention Mechanism 6 2.4 Knowledge Graph Embedding 7 Chapter 3 Methodology 8 3.1 Attention Based Model 9 3.2 Concept Extraction 9 3.3 Concept Similarity Calculation 10 3.4 Document Embedding Calculation 12 3.5 Adaptive Fusion 12 3.6 Prediction Layer 13 Chapter 4 Experiments 14 4.1 Dataset 14 4.1.1 MIMICIII 14 4.1.2 UMLS knowledge graph 15 4.2 Experimental Settings 16 4.2.1 Texts preprocessing 16 4.2.2 Concept extraction 16 4.2.3 Knowledge graph embedding 16 4.2.3.1 TransE 17 4.2.3.2 TransR 17 4.2.3.3 TransD 17 4.2.3.4 RotatE 18 4.2.4 Model parameter settings 18 4.2.5 Baseline model 18 4.2.5.1 CAML 18 4.2.5.2 MultiResCNN 19 4.2.5.3 G_Coder 19 4.3 Results 19 4.3.1 ICD9 Prediction 19 4.3.2 Comparison with Knowledge Graph Model 20 4.3.3 Knowledge Graph Embeddings 21 4.3.4 Prediction Results Grouped by ICD9 Frequency 21 4.3.5 Case Study 22 4.3.5.1 Concept extraction 22 4.3.5.2 Attention weight 24 4.3.5.3 Positive prediction 25 4.3.5.4 Negative prediction 26 Chapter 5 Conclusions 29 5.1 Conclusions 29 5.2 Future Work 30 References 31 | |
| dc.language.iso | en | |
| dc.subject | 知識圖譜嵌入 | zh_TW |
| dc.subject | 醫療編碼預測 | zh_TW |
| dc.subject | 國際疾病分類 | zh_TW |
| dc.subject | 注意力機制 | zh_TW |
| dc.subject | ICD Coding | en |
| dc.subject | Knowledge Graph Embedding | en |
| dc.subject | Attention Mechanism | en |
| dc.subject | Medical Code Prediction | en |
| dc.title | 結合醫學專業知識幫助醫療編碼預測 | zh_TW |
| dc.title | Incorporating Medical Domain Knowledge into Clinical Code Prediction | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳信希(Hsin-Tsai Liu),曾新穆(Chih-Yang Tseng),陳建錦,邱飄逸 | |
| dc.subject.keyword | 醫療編碼預測,國際疾病分類,注意力機制,知識圖譜嵌入, | zh_TW |
| dc.subject.keyword | Medical Code Prediction,ICD Coding,Attention Mechanism,Knowledge Graph Embedding, | en |
| dc.relation.page | 33 | |
| dc.identifier.doi | 10.6342/NTU202102125 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-08-16 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2023-12-31 | - |
| 顯示於系所單位： | 資訊工程學系 | |
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| U0001-0508202119215700.pdf | 1.41 MB | Adobe PDF | 檢視/開啟 |
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