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：

Export PDF Favorites Scan Get Citation

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

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, 朱曉東, 等. Lattice LSTM神經網絡法中文醫學文本命名實體識別模型研究. 中國衛生信息管理雜志, 2019, 16(1): 84-88.
4.	張笑天. 基于Lattice LSTM的醫學文本中文命名實體識別研究與實現. 成都: 電子科技大學, 2019.
5.	王序文, 李姣, 吳英杰, 等. 基于BiLSTM-CRF的中文生物醫學開放式概念關系抽取. 中華醫學圖書情報雜志, 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.	黃夢醒, 李夢龍, 韓惠蕊. 基于電子病歷的實體識別和知識圖譜構建的研究. 計算機應用研究, 2019, 36(12): 3735-3739.
8.	楊紅梅, 李琳, 楊日東, 等. 基于雙向LSTM神經網絡電子病歷命名實體的識別模型. 中國組織工程研究, 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.	陳美杉, 夏晨曦. 肝癌患者在線提問的命名實體識別研究: 一種基于遷移學習的方法. 數據分析與知識發現, 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.	謝尚欣. 基于深度學習的蛋白質二級結構預測. 杭州: 浙江理工大學, 2017.
26.	王劍, 成金勇, 趙志剛, 等. 基于CNN與LSTM模型的蛋白質二級結構預測. 生物信息學, 2018, 16(2): 130-136.
27.	郭延哺, 李維華, 王兵益, 等. 基于卷積長短時記憶神經網絡的蛋白質二級結構預測. 模式識別與人工智能, 2018, 31(6): 562-568.
28.	吳輝. 利用序列信息預測蛋白質二級結構的深度學習模型研究. 天津: 天津大學, 2017.
29.	曹成遠, 呂強. 使用雙向LSTM的深度神經網絡預測蛋白質殘基相互作用. 小型微型計算機系統, 2017, 38(3): 531-535.
30.	凌少平. 基于遞歸神經網的蛋白質結構域預測方法研究. 湘潭: 湘潭大學, 2007.
31.	黃易初. 基于深度學習的蛋白質結構域邊界預測研究. 武漢: 華中科技大學, 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.	王帥, 蔡磊鑫, 顧倜, 等. 運用雙向LSTM擬合RNA二級結構打分函數. 計算機應用與軟件, 2017, 34(9): 232-239.
35.	姜鵬. 多態性位點和致病基因的檢測模型構建與算法研究. 南寧: 廣西大學, 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.	范光鵬, 孫仁誠, 邵峰晶. HIV-1蛋白酶切割位點預測研究. 青島大學學報(工程技術版), 2018, 33(2): 1-6.
38.	張婭楠, 趙涓涓, 趙鑫, 等. 多模態融合下長時程肺部病灶良惡性預測方法. 計算機工程與應用, 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.	安瑩瑩. 基于深度學習的小兒白內障裂隙圖像診斷研究及治療效果預測. 西安: 西安電子科技大學, 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的心律失常分類研究. 蘭州: 蘭州大學, 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. 基于半監督與時序模型的腦電信號特征提取方法研究. 濟南: 山東大學, 2018.
53.	安恩瑩. 基于時序信息的腦電信號分類. 北京: 北京郵電大學, 2018.
54.	張秀麗, 夏斌. 基于CNN-LSTM網絡的睡眠分期研究. 微型機與應用, 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.	郭彥杰. 基于循環神經網絡的脈搏信號分析研究. 北京: 北京郵電大學, 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.
62.	Chen T, Zhang X, Jiang H, et al. Are you smoking? Automatic alert system helping people keep away from cigarettes. Smart Health, 2018, 9-10: 158-169.
63.	朱靜陽. 基于LDBN的心臟病發病風險模型研究. 鄭州: 鄭州大學, 2017.
64.	陳德華, 殷蘇娜, 樂嘉錦, 等. 一種面向臨床領域時序知識圖譜的鏈接預測模型. 計算機研究與發展, 2017, 54(12): 2687-2697.
65.	Kam HJ, Kim HY. Learning representations for the early detection of sepsis with deep neural networks. Comput Biol Med, 2017, 89: 248-255.
66.	鄭亞鵬, 樊璐. 基于LSTM的臨床血液需求預測方法. 計算機與現代化, 2018(5): 41-44, 120.
67.	柴龍凱. 基于數據（序列模式）挖掘的醫院物資使用量預測模型研究. 青島: 青島科技大學, 2018.
68.	Reddy BK, Delen D. Predicting hospital readmission for lupus patients: an RNN-LSTM-based deep-learning methodology. Comput Biol Med, 2018, 101: 199-209.
69.	盧鵬飛, 須成杰, 張敬誼, 等. 基于SARIMA-LSTM的門診量預測研究. 大數據, 2019, 5(6): 101-110.
70.	韓天齊, 宋波. 基于LSTM神經網絡的麻疹發病率預測. 電腦與電信, 2018(5): 54-57.

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, 朱曉東, 等. Lattice LSTM神經網絡法中文醫學文本命名實體識別模型研究. 中國衛生信息管理雜志, 2019, 16(1): 84-88.
4. 張笑天. 基于Lattice LSTM的醫學文本中文命名實體識別研究與實現. 成都: 電子科技大學, 2019.
5. 王序文, 李姣, 吳英杰, 等. 基于BiLSTM-CRF的中文生物醫學開放式概念關系抽取. 中華醫學圖書情報雜志, 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. 黃夢醒, 李夢龍, 韓惠蕊. 基于電子病歷的實體識別和知識圖譜構建的研究. 計算機應用研究, 2019, 36(12): 3735-3739.
8. 楊紅梅, 李琳, 楊日東, 等. 基于雙向LSTM神經網絡電子病歷命名實體的識別模型. 中國組織工程研究, 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. 陳美杉, 夏晨曦. 肝癌患者在線提問的命名實體識別研究: 一種基于遷移學習的方法. 數據分析與知識發現, 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. 謝尚欣. 基于深度學習的蛋白質二級結構預測. 杭州: 浙江理工大學, 2017.
26. 王劍, 成金勇, 趙志剛, 等. 基于CNN與LSTM模型的蛋白質二級結構預測. 生物信息學, 2018, 16(2): 130-136.
27. 郭延哺, 李維華, 王兵益, 等. 基于卷積長短時記憶神經網絡的蛋白質二級結構預測. 模式識別與人工智能, 2018, 31(6): 562-568.
28. 吳輝. 利用序列信息預測蛋白質二級結構的深度學習模型研究. 天津: 天津大學, 2017.
29. 曹成遠, 呂強. 使用雙向LSTM的深度神經網絡預測蛋白質殘基相互作用. 小型微型計算機系統, 2017, 38(3): 531-535.
30. 凌少平. 基于遞歸神經網的蛋白質結構域預測方法研究. 湘潭: 湘潭大學, 2007.
31. 黃易初. 基于深度學習的蛋白質結構域邊界預測研究. 武漢: 華中科技大學, 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. 王帥, 蔡磊鑫, 顧倜, 等. 運用雙向LSTM擬合RNA二級結構打分函數. 計算機應用與軟件, 2017, 34(9): 232-239.
35. 姜鵬. 多態性位點和致病基因的檢測模型構建與算法研究. 南寧: 廣西大學, 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. 范光鵬, 孫仁誠, 邵峰晶. HIV-1蛋白酶切割位點預測研究. 青島大學學報(工程技術版), 2018, 33(2): 1-6.
38. 張婭楠, 趙涓涓, 趙鑫, 等. 多模態融合下長時程肺部病灶良惡性預測方法. 計算機工程與應用, 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. 安瑩瑩. 基于深度學習的小兒白內障裂隙圖像診斷研究及治療效果預測. 西安: 西安電子科技大學, 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的心律失常分類研究. 蘭州: 蘭州大學, 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. 基于半監督與時序模型的腦電信號特征提取方法研究. 濟南: 山東大學, 2018.
53. 安恩瑩. 基于時序信息的腦電信號分類. 北京: 北京郵電大學, 2018.
54. 張秀麗, 夏斌. 基于CNN-LSTM網絡的睡眠分期研究. 微型機與應用, 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. 郭彥杰. 基于循環神經網絡的脈搏信號分析研究. 北京: 北京郵電大學, 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.
62. Chen T, Zhang X, Jiang H, et al. Are you smoking? Automatic alert system helping people keep away from cigarettes. Smart Health, 2018, 9-10: 158-169.
63. 朱靜陽. 基于LDBN的心臟病發病風險模型研究. 鄭州: 鄭州大學, 2017.
64. 陳德華, 殷蘇娜, 樂嘉錦, 等. 一種面向臨床領域時序知識圖譜的鏈接預測模型. 計算機研究與發展, 2017, 54(12): 2687-2697.
65. Kam HJ, Kim HY. Learning representations for the early detection of sepsis with deep neural networks. Comput Biol Med, 2017, 89: 248-255.
66. 鄭亞鵬, 樊璐. 基于LSTM的臨床血液需求預測方法. 計算機與現代化, 2018(5): 41-44, 120.
67. 柴龍凱. 基于數據（序列模式）挖掘的醫院物資使用量預測模型研究. 青島: 青島科技大學, 2018.
68. Reddy BK, Delen D. Predicting hospital readmission for lupus patients: an RNN-LSTM-based deep-learning methodology. Comput Biol Med, 2018, 101: 199-209.
69. 盧鵬飛, 須成杰, 張敬誼, 等. 基于SARIMA-LSTM的門診量預測研究. 大數據, 2019, 5(6): 101-110.
70. 韓天齊, 宋波. 基于LSTM神經網絡的麻疹發病率預測. 電腦與電信, 2018(5): 54-57.

West China Medical Journal

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

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content