Current applications of long short-term memory networks in medical and biomedical fields_West China Medical Journal

Authors：

ZENG Yu ,  YANG Xiaoyan ,  ZHANG Wei

West China Biomedical Big Data Center, West China Hospital / West China School of Medicine, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

YANG Xiaoyan, Email: yangxiaoyan@scu.edu.cn; ZHANG Wei, Email: zhangwei@wchscu.cn

Keywords：

Long short-term memory network; Medicine; Public health; Biomedicine

DOI：

10.7507/1002-0179.202004036

Video：

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Abstract Full text Figures/Tables Video References Cited by

The rapid development of medical informatization and continuous innovation of artificial intelligence have made it possible to analyze data and predict prognosis through making full use of data analysis or data mining methods in medical field, which can provide not only more accurate basis of diagnosis and treatment for patients but also important decision-making reference for the government and hospitals to allocate medical resources reasonably. As a classical model for processing time series data in machine learning, long short-term memory network can break through some limitations of statistics to process large and complex medical data. The current applications of long short-term memory networks in medical and biomedical fields can be mainly summarized as seven themes, including natural language processing, biomedical information, signals, motion, clinical medical records, hospital management, and public health and policy.

Citation： ZENG Yu, YANG Xiaoyan, ZHANG Wei. Current applications of long short-term memory networks in medical and biomedical fields. West China Medical Journal, 2021, 36(1): 131-136. doi: 10.7507/1002-0179.202004036 Copy

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1. Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw, 1994, 5(2): 157-166.
2. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735-1780.
3. 王博冉, 林夏, 朱晓东, 等. Lattice LSTM神经网络法中文医学文本命名实体识别模型研究. 中国卫生信息管理杂志, 2019, 16(1): 84-88.
4. 张笑天. 基于Lattice LSTM的医学文本中文命名实体识别研究与实现. 成都: 电子科技大学, 2019.
5. 王序文, 李姣, 吴英杰, 等. 基于BiLSTM-CRF的中文生物医学开放式概念关系抽取. 中华医学图书情报杂志, 2018, 27(11): 33-39.
6. Li PL, Yuan ZM, Tu WN, et al. Medical knowledge extraction and analysis from electronic medical records using deep learning. Chin Med Sci J, 2019, 34(2): 133-139.
7. 黄梦醒, 李梦龙, 韩惠蕊. 基于电子病历的实体识别和知识图谱构建的研究. 计算机应用研究, 2019, 36(12): 3735-3739.
8. 杨红梅, 李琳, 杨日东, 等. 基于双向LSTM神经网络电子病历命名实体的识别模型. 中国组织工程研究, 2018, 22(20): 3237-3242.
9. Liu Z, Yang M, Wang XL, et al. Entity recognition from clinical texts via recurrent neural network. BMC Med Inform Decis Mak, 2017, 17(Suppl 2): 67.
10. Zhao YS, Zhang KL, Ma HC, et al. Leveraging text skeleton for de-identification of electronic medical records. BMC Med Inform Decis Mak, 2018, 18(Suppl 1): 18.
11. Jiang Z, Zhao C, He B, et al. De-identification of medical records using conditional random fields and long short-term memory networks. J Biomed Inform, 2017, 75S: S43-S53.
12. 陈美杉, 夏晨曦. 肝癌患者在线提问的命名实体识别研究: 一种基于迁移学习的方法. 数据分析与知识发现, 2019, 3(12): 61-69.
13. Abatemarco D, Perera S, Bao SH, et al. Training augmented intelligent capabilities for pharmacovigilance: applying deep-learning approaches to individual case safety report processing. Pharmaceut Med, 2018, 32(6): 391-401.
14. Zhou D, Miao L, He Y. Position-aware deep multi-task learning for drug-drug interaction extraction. Artif Intell Med, 2018, 87: 1-8.
15. Zheng W, Lin H, Luo L, et al. An attention-based effective neural model for drug-drug interactions extraction. BMC Bioinformatics, 2017, 18(1): 445.
16. Huang D, Jiang Z, Zou L, et al. Drug-drug interaction extraction from biomedical literature using support vector machine and long short term memory networks. Inf Sci, 2017, 415-416: 100-109.
17. Li H, Yang M, Chen QC, et al. Chemical-induced disease extraction via recurrent piecewise convolutional neural networks. BMC Med Inform Decis Mak, 2018, 18(Suppl 2): 60.
18. Weng WH, Wagholikar KB, Mccray AT, et al. Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach. BMC Med Inform Decis Mak, 2017, 17(1): 155.
19. Luo Y. Recurrent neural networks for classifying relations in clinical notes. J Biomed Inform, 2017, 72: 85-95.
20. Hu Y, Wen GH, Ma JJ, et al. Label-indicator morpheme growth on LSTM for Chinese healthcare question department classification. J Biomed Inform, 2018, 82: 154-168.
21. Tahmasebi AM, Zhu H, Mankovich G, et al. Automatic normalization of anatomical phrases in radiology reports using unsupervised learning. J Digit Imaging, 2019, 32(1): 6-18.
22. Dergachyova O, Morandi X, Jannin P. Knowledge transfer for surgical activity prediction. Int J Comput Assist Radiol Surg, 2018, 13(9): 1409-1417.
23. Chen H, Gangaram V, Shih G. Developing a more responsive radiology resident dashboard. J Digit Imaging, 2019, 32(1): 81-90.
24. Wang H, Li C, Zhang JH, et al. A new LSTM-based gene expression prediction model: L-GEPM. J Bioinform Comput Biol, 2019, 17(4): 1950022.
25. 谢尚欣. 基于深度学习的蛋白质二级结构预测. 杭州: 浙江理工大学, 2017.
26. 王剑, 成金勇, 赵志刚, 等. 基于CNN与LSTM模型的蛋白质二级结构预测. 生物信息学, 2018, 16(2): 130-136.
27. 郭延哺, 李维华, 王兵益, 等. 基于卷积长短时记忆神经网络的蛋白质二级结构预测. 模式识别与人工智能, 2018, 31(6): 562-568.
28. 吴辉. 利用序列信息预测蛋白质二级结构的深度学习模型研究. 天津: 天津大学, 2017.
29. 曹成远, 吕强. 使用双向LSTM的深度神经网络预测蛋白质残基相互作用. 小型微型计算机系统, 2017, 38(3): 531-535.
30. 凌少平. 基于递归神经网的蛋白质结构域预测方法研究. 湘潭: 湘潭大学, 2007.
31. 黄易初. 基于深度学习的蛋白质结构域边界预测研究. 武汉: 华中科技大学, 2016.
32. Hochreiter S, Heusel M, Obermayer K. Fast model-based protein homology detection without alignment. Bioinformatics, 2007, 23(14): 1728-1736.
33. Li S, Chen J, Liu B. Protein remote homology detection based on bidirectional long short-term memory. BMC Bioinformatics, 2017, 18(1): 443.
34. 王帅, 蔡磊鑫, 顾倜, 等. 运用双向LSTM拟合RNA二级结构打分函数. 计算机应用与软件, 2017, 34(9): 232-239.
35. 姜鹏. 多态性位点和致病基因的检测模型构建与算法研究. 南宁: 广西大学, 2017.
36. Nagarajan D, Nagarajan T, Roy N, et al. Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria. J Biol Chem, 2018, 293(10): 3492-3509.
37. 范光鹏, 孙仁诚, 邵峰晶. HIV-1蛋白酶切割位点预测研究. 青岛大学学报(工程技术版), 2018, 33(2): 1-6.
38. 张娅楠, 赵涓涓, 赵鑫, 等. 多模态融合下长时程肺部病灶良恶性预测方法. 计算机工程与应用, 2019, 55(10): 146-153.
39. Han Z, Wei BZ, Mercado A, et al. Spine-GAN: semantic segmentation of multiple spinal structures. Med Image Anal, 2018, 50: 23-35.
40. Pei M, Wu X, Guo Y, et al. Small bowel motility assessment based on fully convolutional networks and long short-term memory. Knowl Based Syst, 2017, 121: 163-172.
41. He X, Yang Y, Shi B, et al. VD-SAN: visual-densely semantic attention network for image caption generation. Neurocomputing, 2018, 328(7): 48-55.
42. 安莹莹. 基于深度学习的小儿白内障裂隙图像诊断研究及治疗效果预测. 西安: 西安电子科技大学, 2017.
43. Azizi S, Van WN, Sojoudi S, et al. Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy. Int J Comput Assist Radiol Surg, 2018, 13(8): 1201-1209.
44. Ahmedt-Aristizabal D, Fookes C, Nguyen K, et al. Deep facial analysis: a new phase I epilepsy evaluation using computer vision. Epilepsy Behav, 2018, 82: 17-24.
45. Oh SL, Ng E, Tan RS, et al. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Comput Biol Med, 2018, 102: 278-287.
46. Swapna G, Soman KP, Vinayakumar R. Automated detection of cardiac arrhythmia using deep learning techniques. Procedia Comput Sci, 2018, 132: 1192-1201.
47. Andersen RS, Peimankar A, Puthusserypady S. A deep learning approach for real-time detection of atrial fibrillation. Expert Syst Appl, 2018, 115: 465-473.
48. 李雪. 基于LSTM的心律失常分类研究. 兰州: 兰州大学, 2018.
49. Swapna G, Soman Kp, Vinayakumar R. Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals. Procedia Comp Sci, 2018, 132: 1253-1262.
50. Tan JH, Hagiwara Y, Pang W, et al. Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals. Comput Biol Med, 2018, 94: 19-26.
51. Qiu Y, Huang K, Xiao F, et al. A segment-wise reconstruction method based on bidirectional long short term memory for power line interference suppression. Biocybern Biomed Eng, 2018, 38(2): 217-227.
52. 辛雨航. 基于半监督与时序模型的脑电信号特征提取方法研究. 济南: 山东大学, 2018.
53. 安恩莹. 基于时序信息的脑电信号分类. 北京: 北京邮电大学, 2018.
54. 张秀丽, 夏斌. 基于CNN-LSTM网络的睡眠分期研究. 微型机与应用, 2017, 36(17): 88-91.
55. Tsiouris ΚΜ, Pezoulas VC, Zervakis M, et al. A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals. Comput Biol Med, 2018, 99: 24-37.
56. Li Y, Charalampaki P, Liu Y, et al. Context aware decision support in neurosurgical oncology based on an efficient classification of endomicroscopic data. Int J Comput Assist Radiol Surg, 2018, 13(8): 1187-1199.
57. 郭彦杰. 基于循环神经网络的脉搏信号分析研究. 北京: 北京邮电大学, 2018.
58. Zhao A, Qi L, Dong J, et al. Dual channel LSTM based multi-feature extraction in gait for diagnosis of neurodegenerative diseases. Knowl Based Syst, 2018, 145: 91-97.
59. Medina-Quero J, Zhang S, Nugent C, et al. Ensemble classifier of long short-term memory with fuzzy temporal windows on binary sensors for activity recognition. Expert Syst Appl, 2018, 114: 441-453.
60. Taramasco C, Lazo Y, Rodenas T, et al. System design for emergency alert triggered by falls using convolutional neural networks. J Med Syst, 2020, 44(2): 50.
61. Liu ZC, Ling ZH, Dai LR. Articulatory-to-acoustic conversion using BLSTM-RNNs with augmented input representation. Speech Commun, 2018, 99: 161-172.
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West China Medical Journal

Current applications of long short-term memory networks in medical and biomedical fields

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