An Improved Medical Text Classification Model: LS-GRU

doi:10.12068/j.issn.1005-3026.2020.07.005

Journal of Northeastern University Natural Science ›› 2020, Vol. 41 ›› Issue (7): 938-943.DOI: 10.12068/j.issn.1005-3026.2020.07.005

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An Improved Medical Text Classification Model: LS-GRU

LI Qiang¹， LI Yao-kun²， XIA Shu-yue³， KANG Yan^1,4

1. College of Medicine and Biological Information Engineering， Northeastern University， Shenyang 110169， China; 2. China Petroleum Pipeline Corporation， Langfang 065000， China; 3. The Central Hospital Affiliated to Shenyang Medical， Shenyang 110024， China; 4. College of Health Science and Environmental Engineering， Shenzhen Technology University， Shenzhen 518118， China.

Received:2019-08-10 Revised:2019-08-10 Online:2020-07-15 Published:2020-07-15
Contact: KANG Yan
About author:-
Supported by:
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Abstract

Abstract: In order to help radiologists report the CT image results more accurately and effectively to the clinicians， an improved GRU deep learning framework LS-GRU was proposed to solve the classification of image report text， which can be automatically fed back to clinicians according to radiologists’ descriptions. The data was collected from more than 1168 cases of respiratory imaging reports. Two diseases(emphysema and pneumonia) with similar descriptions of radiologists were classified. About 652 cases of emphysema and 516 cases of pneumonia were reported. The GRU， BiGRU and LSTM models were validated， respectively. The results show that the LS-GRU model is more accurate and robust.

Key words: deep learning, medical text classification, GRU(gate recurrent unit), emphysema, LSTM(long-short term memory)

CLC Number:

TG335.58

LI Qiang， LI Yao-kun， XIA Shu-yue， KANG Yan. An Improved Medical Text Classification Model: LS-GRU[J]. Journal of Northeastern University Natural Science, 2020, 41(7): 938-943.

References

[1]Yu S，Indurthi S R，Back S，et al.A multi-stage memory augmented neural network for machine reading comprehension［C］//Proceedings of the Workshop on Machine Reading for Question Answering.Melbourne，2018:21-30.
[2]Vaswani A，Shazeer N，Parmar N，et al.Attention is all you need［C］//Advances in Neural Information Processing Systems.Long Beach，2017:5998-6008.
[3]Lin Y，Shen S，Liu Z，et al.Neural relation extraction with selective attention over instances［C］//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.Berlin，2016:2124-2133.
[4]陈志豪，刘子辰，邱大伟，等.基于注意力和字嵌入的中文医疗问答匹配方法［J］.计算机应用，2019，39(6):1639-1645.(Chen Zhi-hao，Liu Zi-chen，Qiu Da-wei，et al.Chinese medical question-and-answer matching method based on attention and word embedding［J］.Computer Application，2019，39(6):1639-1645.)
[5]张浩宇，张鹏飞，李真真，等.基于自注意力机制的阅读理解模型［J］.中文信息学报，2018，32(12):125-131.(Zhang Hao-yu，Zhang Peng-fei，Li Zhen-zhen，et al.Self-attention based machine reading comprehension［J］.Journal of Chinese Information Processing，2018，32(12):125-131.)
[6]Kowsari K，Brown D E，Heidarysafa M，et al.Hdltex:hierarchical deep learning for text classification［C］//2017 16th IEEE International Conference on Machine Learning and Applications.Cancun:IEEE，2017:364-371.
[7]Hochreiter S.The vanishing gradient problem during learning recurrent neural nets and problem solutions［J］.International Journal of Uncertainty，Fuzziness and Knowledge-Based Systems，1998，6(2):107-116.
[8]Zhou P，Shi W，Tian J，et al.Attention-based bidirectional long short-term memory networks for relation classification［C］//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.Berlin，2016:207-212.
[9]Huang E H，Socher R，Manning C D，et al.Improving word representations via global context and multiple word prototypes［C］//Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics.Jeju Island，2012:873-882.

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An Improved Medical Text Classification Model: LS-GRU

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