[1]ZHANG Heng,HE Wenbin,HE Jun,et al.Multi-task tumor stage learning model with medical knowledge enhancement[J].CAAI Transactions on Intelligent Systems,2021,16(4):739-745.[doi:10.11992/tis.202010005]
Copy

Multi-task tumor stage learning model with medical knowledge enhancement

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 4 Page number: 739-745 Column: 学术论文—知识工程 Public date: 2021-07-05
Title:
Multi-task tumor stage learning model with medical knowledge enhancement
Author(s):
ZHANG Heng1;  HE Wenbin2;  HE Jun1;  JIAO Zengtao2;  LIU Hongyan3
1. School of Information, Renmin University of China, Beijing 100872, China;
2. Yidu Cloud (Beijing) Technology Co., Ltd, Beijing 100191, China;
3. Department of Management Science and Technology, Tsinghua University, Beijing 100084, China
Keywords:
tumor staging; text classification; machine reading comprehension; multi-task learning; unbalanced classification; smart healthcare; knowledge representation; attention mechanism
CLC:
TP391
DOI:
10.11992/tis.202010005
Abstract:
Tumor staging is the process of inferring the corresponding stage of tumors based on patients’ electronic health records (EHR). The serious uneven data distribution in the types of EHRs has certain challenges on tumor stage prediction through in-depth learning. Accordingly, this paper proposes a knowledge enhanced multi-task (KEMT) model and considers tumor stage reasoning as a text classification task of EHR. It also introduces medical attributes that doctors referred to in tumor stage prediction and introduces a medical problem-based machine reading comprehension task. The tasks are jointly studied by building a real-world dataset of tumor staging with medical institutions. Experimental results show that the KEMT model combines medical knowledge with a neural network and gets a higher precision rate of prediction than the traditional text classification models. Under the condition of uneven data distribution, the accuracy of small samples is improved by 4.2%, for which the model also accounts.
References:
[1] 姚云峰. 肿瘤分期与疗效评价[J]. 中国医学前沿杂志(电子版), 2010, 2(4): 70-75
YAO Yunfeng. Evaluation of tumor stage and curative effect[J]. Chinese journal of the frontiers of medical science (electronic version), 2010, 2(4): 70-75
[2] 周斌, 季科, 辛灵, 等. 美国肿瘤联合会乳腺癌分期系统(第8版)更新内容介绍及解读[J]. 中国实用外科杂志, 2017, 37(1): 10-14
ZHOU Bin, JI Ke, XIN Ling, et al. Updates and interpretations of the 8th edition of AJCC breast cancer staging system[J]. Chinese journal of practical surgery, 2017, 37(1): 10-14
[3] HU Zikun, LI Xiang, TU Cunchao, et al. Few-shot charge prediction with discriminative legal attributes[C]//Proceedings of the 27th International Conference on Computational Linguistics. New Mexico, USA, 2018: 487-498.
[4] KIM Y. Convolutional neural networks for sentence classification[C]//Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing. Doha, Qatar, 2014: 1746-1751.
[5] TANG Duyu, QIN Bing, LIU Ting. Document modeling with gated recurrent neural network for sentiment classification[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon, Portugal, 2015: 1422-1432.
[6] JOULIN A, GRAVE E, BOJANOWSKI P, et al. Bag of tricks for efficient text classification[C]//Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Valencia, Spain, 2017: 427-431.
[7] JOHNSON R, ZHANG Tong. Deep pyramid convolutional neural networks for text categorization[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Vancouver, Canada, 2017: 562-570.
[8] YAO Liang, MAO Chengsheng, LUO Yuang. Graph convolutional networks for text classification[C]//Proceedings of 32rd AAAI Conference on Artificial Intelligence. Hawaii, USA: 7370-7377.
[9] SUN Chi, QIU Xipeng, XU Yige, et al. How to fine-tune BERT for text classification?[C]//Proceedings of the 18th China National Conference on Chinese Computational Linguistics. Kunming, China, 2019: 194-206.
[10] ELHOSEINY M, SALEH B, ELGAMMAL A. Write a classifier: zero-shot learning using purely textual descriptions[C]//Proceedings of 2013 IEEE International Conference on Computer Vision. Sydney, Australia, 2013: 2584-2591.
[11] CUI Yiming, CHEN Zhipeng, WEI Si, et al. Attention-over-attention neural networks for reading comprehension[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Vancouver, Canada, 2017: 593-602.
[12] SEO M, KEMBHAVI A, FARHADI A, et al. Bidirectional attention flow for machine comprehension[EB/OL].(2016-11-05) [2019-10-12] https://arxiv.org/abs/1611.01603.
[13] PASZKE A, GROSS S, CHINTALA S, et al. Automatic differentiation in PyTorch[C]//Proceedings of the 31st Conference on Neural Information Processing Systems. Long Beach, USA, 2017.
[14] KINGMA D P, BA J. Adam: a method for stochastic optimization[EB/OL]. (2014-12-22) [2019-12-12] https://arxiv.org/pdf/1412.6980.pdf.
[15] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. The journal of machine learning research, 2014, 15(1): 1929-1958.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems