Computer Science ›› 2020, Vol. 47 ›› Issue (9): 185-189.doi: 10.11896/jsjkx.190900001

• Artificial Intelligence • Previous Articles     Next Articles

MTHAM:Multitask Disease Progression Modeling Based on Hierarchical Attention Mechanism

PAN Zu-jiang1, LIU Ning1, ZHANG Wei2, WANG Jian-yong1   

  1. 1 Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China
    2 School of Computer Science and Technology,East China Normal University,Shanghai 200333,China
  • Received:2019-08-30 Published:2020-09-10
  • About author:PAN Zu-jiang,born in 1994,postgra-duate.His research interests include data mining and machine learnig.
    ZHANG Wei,born in 1988,Ph.D,associate researcher.His research interests include user data modeling and so on.
  • Supported by:
    Key Program of National Natural Science Foundation of China (61532010).

Abstract: Alzheimer’s disease (AD) is an irreversible neurodegenerative disease.The degeneration of brain tissue causes serious cognitive problems and eventually leads to death.There are many clinical trials and research projects to study AD pathology and produce some data for analysis.This paper focuses on the diagnosis of AD and the prediction of potential prognosis in combination with a variety of clinical features.In this paper,a multi-task disease progression model based on hierarchical attention mechanism is proposed.The task of disease automatic diagnosis is regarded as the main task,and the task of disease prognosis is regarded as the auxiliary task to improve the generalization ability of the model,and then improve the performance of disease automatic diagnosis task.In this paper,two layers of attention mechanism are applied in the feature layer and the medical record layer respectively,so that the model can pay different attention to different features and different medical records.The validation experiment is carried out on ADNI (Alzheimer’s Disease Neuroimaging Initiative) dataset.Compared with several benchmark models,the experimental results show that the proposed method has better performance and provides better robustness for clinical application.

Key words: Alzheimer’s disease, Attention mechanism, Automatic diagnosis, Multi-task learning, Prognosis prediction

CLC Number: 

  • TP391.4
[1] PATTERSON C.World Alzheimer Report 2018—The state of the art of dementia research:New frontiers[R].Alzheimer’s Disease International (ADI):London,UK,2018.
[2] WANG Q,SUN M,ZHAN L,et al.Multi-Modality DiseaseModeling via Collective Deep Matrix Factorization[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2017:1155-1164.
[3] DAI P,GWADRY-SRIDHAR F,BAUER M,et al.Healthy cognitive aging:A hybrid random vector functional-link model for the analysis of alzheimer’s disease[C]//Thirty-First AAAI Conference on Artificial Intelligence.2017.
[4] HUANG G B,ZHU Q Y,SIEW C K.Extreme learning ma-chine:theory and applications[J].Neurocomputing,2006,70(1/2/3):489-501.
[5] HINTON G,DENG L,YU D,et al.Deep neural networks for acoustic modeling in speech recognition[J].IEEE Signal proces-sing magazine,2012,29(6):82-97.
[6] LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436.
[7] TUFAIL A B,ABIDI A,SIDDIQUI A M,et al.Automatic classification of initial categories of Alzheimer’s disease from structural MRI phase images:a comparison of PSVM,KNN and ANN methods[J].Age,2012,2012:1731.
[8] LEBEDEV A V,WESTMAN E,VAN WESTEN G J P,et al.Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness[J].NeuroImage:Clinical,2014,6:115-125.
[9] LÓPEZ M,RAMÍREZ J,GÓRRIZ J M,et al.Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer's disease[J].Neurocomputing,2011,74(8):1260-1271.
[10] SHI B,CHEN Y,HOBBS K,et al.Nonlinear Metric Learning for Alzheimer’s Disease Diagnosis with Integration of Longitudinal Neuroimaging Features[C]//BMVC.2015.
[11] DAI P,GWADRY-SRIDHAR F,BAUER M,et al.Bagging ensembles for the diagnosis and prognostication of alzheimer’s di-sease[C]//Thirtieth AAAI Conference on Artificial Intelligence.2016.
[12] XING E P,JORDAN M I,RUSSELL S J,et al.Distance metric learning with application to clustering with side-information[C]//Advances in neural information processing systems.2003:521-528.
[13] LIN T,ZHA H.Riemannian manifold learning[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,30(5):796-809.
[14] 邱锡鹏.神经网络与深度学习[OL].[2017-04-21].
[15] BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[J].arXiv:1409.0473,2014.
[16] YANG Z,YANG D,DYER C,et al.Hierarchical attention networks for document classification[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2016:1480-1489.
[17] MNIH V,HEESS N,GRAVES A.Recurrent models of visualattention[C]//Advances in Neural Information Processing Systems.2014:2204-2212.
[18] YANG Y,YANG L,ZOU Y B,et al.Humor Recognition Based on Linguistic Features and Hierarchical Attention Mechanism[J].Computer Engineering,2020,46(8):64-71.
[19] CHOI E,BAHADORI M T,SUN J,et al.Retain:An interpretable predictive model for healthcare using reverse time attention mechanism[C]//Advances in Neural Information Processing Systems.2016:3504-3512.
[20] COLLOBERT R,WESTON J.A unified architecture for natural language processing:Deep neural networks with multitask lear-ning[C]//Proceedings of the 25th International Conference on Machine Learning.ACM,2008:160-167.
[21] ZHANG W J.An Online Multi-Task Learning Algorithm Based on Weight Matrix Decomposition[J].Computer Engineering,2019,45(8):190-197.
[22] DENG L,HINTON G,KINGSBURY B.New types of deep neural network learning for speech recognition and related applications:An overview[C]//2013 IEEE International Conference on Acoustics,Speech and Signal Processing.IEEE,2013:8599-8603.
[23] GIRSHICK R.Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:1440-1448.
[24] RAMSUNDAR B,KEARNES S,RILEY P,et al.Massivelymultitask networks for drug discovery[J].arXiv:1502.02072,2015.
[25] RUDER S.An overview of multi-task learning in deep neuralnetworks[J].arXiv:1706.05098,2017.
[26] CARUANA R.Multitask learning[J].Machine Learning,1997,28(1):41-75.
[27] BAXTER J.A Bayesian/information theoretic model of learning to learn via multiple task sampling[J].Machine Learning,1997,28(1):7-39.
[28] HOCHREITER S,SCHMIDHUBER J.Long short-term memo-ry[J].Neural Computation,1997,9(8):1735-1780.
[29] KINGMA D P,BA J.Adam:A method for stochastic optimization[J].arXiv:1412.6980,2014.
[30] WEINER M W,VEITCH D P,AISEN P S,et al.The Alzhemer’sDisease Neuroimaging Initiative:a review of papers published since its inception[J].Alzheimer’s & Dementia,2013,9(5):e111-e194.
[1] ZHOU Fang-quan, CHENG Wei-qing. Sequence Recommendation Based on Global Enhanced Graph Neural Network [J]. Computer Science, 2022, 49(9): 55-63.
[2] DAI Yu, XU Lin-feng. Cross-image Text Reading Method Based on Text Line Matching [J]. Computer Science, 2022, 49(9): 139-145.
[3] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[4] XIONG Li-qin, CAO Lei, LAI Jun, CHEN Xi-liang. Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization [J]. Computer Science, 2022, 49(9): 172-182.
[5] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[6] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[7] WANG Ming, PENG Jian, HUANG Fei-hu. Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction [J]. Computer Science, 2022, 49(8): 40-48.
[8] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[9] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[10] YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236.
[11] JIN Fang-yan, WANG Xiu-li. Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM [J]. Computer Science, 2022, 49(7): 179-186.
[12] XIONG Luo-geng, ZHENG Shang, ZOU Hai-tao, YU Hua-long, GAO Shang. Software Self-admitted Technical Debt Identification with Bidirectional Gate Recurrent Unit and Attention Mechanism [J]. Computer Science, 2022, 49(7): 212-219.
[13] PENG Shuang, WU Jiang-jiang, CHEN Hao, DU Chun, LI Jun. Satellite Onboard Observation Task Planning Based on Attention Neural Network [J]. Computer Science, 2022, 49(7): 242-247.
[14] ZHANG Ying-tao, ZHANG Jie, ZHANG Rui, ZHANG Wen-qiang. Photorealistic Style Transfer Guided by Global Information [J]. Computer Science, 2022, 49(7): 100-105.
[15] ZENG Zhi-xian, CAO Jian-jun, WENG Nian-feng, JIANG Guo-quan, XU Bin. Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism [J]. Computer Science, 2022, 49(7): 106-112.
Full text



No Suggested Reading articles found!