Computer Science

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).

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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
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