Adverse drug-drug interaction is a critical safety issue for the development of drugs. In Traditional Chinese Medicine (TCM), adverse herb-herb interaction is regarded as negative reactions in patients after the absor...
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Adverse drug-drug interaction is a critical safety issue for the development of drugs. In Traditional Chinese Medicine (TCM), adverse herb-herb interaction is regarded as negative reactions in patients after the absorption of the decoction of Incompatible Herb Pair (IHP). Recently, many methods are proposed for IHP researches, but most of them focus on revealing and analyzing the adverse reactions of known IHPs, despite that there are still a number of new IHPs discovered by accidents. Up to now, IHPs have become a serious threat to public health in TCM medication. In this paper, we propose a novel supervised learning framework with attribute regularization for IHP prediction. In this framework, we model the prediction task as a non-negative matrix tri-factorization problem, in which two important herb attributes (efficacy and flavor) and their correlation are incorporated to characterize the incompatible relationship between herbs. A hypothetical test method is adopted to evaluate the statistical significance of the dissimilar characteristics of two attributes and the attribute information from the TCM literature is adopted to estimate the correlation between attributes. These two constraints are jointly incorporated as attribute regularizations into the framework to improve IHP prediction. The update solutions and the convergence proof for the optimization problem are given in detail. Experimental results on the real-world IHP datasets demonstrate that the proposed framework is effective for IHP prediction compared with eight baseline methods and its variants. (c) 2019 Elsevier B.V. All rights reserved.
Recently, attributes have been introduced as a kind of high-level semantic information to help improve the classification accuracy. Multitask learning is an effective methodology to achieve this goal, which shares low...
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Recently, attributes have been introduced as a kind of high-level semantic information to help improve the classification accuracy. Multitask learning is an effective methodology to achieve this goal, which shares low-level features between attributes and actions. Yet such methods neglect the constraints that attributes impose on classes, which may fail to constrain the semantic relationship between the attributes and actions. In this paper, we explicitly consider such attribute-action relationship for human action recognition, and correspondingly, we modify the multitask learning model by adding attribute regularization. In this way, the learned model not only shares the low-level features, but also gets regularized according to the semantic constrains. In addition, since attribute and class label contain different amounts of semantic information, we separately treat attribute classifiers and action classifiers in the framework of multitask learning for further performance improvement. Our method is verified on three challenging datasets (KTH, UIUC, and Olympic Sports), and the experimental results demonstrate that our method achieves better results than that of previous methods on human action recognition.
Deep learning (DL) methods where interpretability is intrinsically considered as part of the model are required to better understand the relationship of clinical and imaging-based attributes with DL outcomes, thus fac...
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Deep learning (DL) methods where interpretability is intrinsically considered as part of the model are required to better understand the relationship of clinical and imaging-based attributes with DL outcomes, thus facilitating their use in the reasoning behind the medical decisions. Latent space representations built with variational autoencoders (VAE) do not ensure individual control of data attributes. attribute-based methods enforcing attribute disentanglement have been proposed in the literature for classical computer vision tasks in benchmark data. In this paper, we propose a VAE approach, the Attri-VAE, that includes an attribute regularization term to associate clinical and medical imaging attributes with different regularized dimensions in the generated latent space, enabling a better-disentangled interpretation of the attributes. Furthermore, the generated attention maps explained the attribute encoding in the regularized latent space dimensions. Using the Attri-VAE approach we analyzed healthy and myocardial infarction patients with clinical, cardiac morphology, and radiomics attributes. The proposed model provided an excellent trade-off between reconstruction fidelity, disentanglement, and interpretability, outperforming state-of-the-art VAE approaches according to several quantitative metrics. The resulting latent space allowed the generation of realistic synthetic data in the trajectory between two distinct input samples or along a specific attribute dimension to better interpret changes between different cardiac conditions.
Recently, attributes have been introduced to help object classification. Multi-task learning is an effective methodology to achieve this goal, which shares low-level features between attribute and object classifiers. ...
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ISBN:
(纸本)9780769547978
Recently, attributes have been introduced to help object classification. Multi-task learning is an effective methodology to achieve this goal, which shares low-level features between attribute and object classifiers. Yet such a method neglects the constraints that attributes impose on classes which may fail to constrain the semantic relationship between the attribute and object classifiers. In this paper, we explicitly consider such attribute-object relationship, and correspondingly, we modify the multi-task learning model by adding attribute regularization. In this way, the learned model not only shares the low-level features, but also gets regularized according to the semantic constrains. Our method is verified on two challenging datasets (KTH and Olympic Sports), and the experimental results demonstrate that our method achieves better results than previous methods in human action recognition.
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