MTHAM:Multitask Disease Progression Modeling Based on Hierarchical Attention Mechanism

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 ADpathology and produce some data for analysis．This paper focuses on the diagnosis of AD and the prediction of potential prognosis in combination with avariety of clinical features．In this paper,a multitask 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 generalizationability of the model, and then improve the performance of disease automatic diagnosis task．In this paper,two layer soft 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．

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Recommended citation: Your Name, You. (2009). “Paper Title Number 1.” Journal 1. 1(1).