Dr Lin Yue

Medication recommendation aims to make effective prescriptions based on electronic healthcare records (EHRs) of patients, and assists caregivers in clinical decision making. Obtaining temporal patterns of patient conditions as well as contextual information contained in EHRs are the key issues for the success of recommendation. Existing methods do not take the difference in the amount of medical records of different patients into account, and fails to change the focus or number of iterations during information extraction according to personalized patient conditions. To address these problems, the medication recommendation model adaptive multi-hop reading with selective coverage mechanism (AMHSC) is proposed. The model stores encoded temporal patterns with memory neural networks (MemNN), and applies the selective coverage mechanism to balance attention weights over selected information during the attentive multi-hop reading on MemNN. Meanwhile, AMHSC adaptively determines the number of reading hops on MemNN according to personalized patient conditions. Experiments on real-world clinical dataset demonstrate that AMHSC successfully derives important information from EHRs to build informative patient representations for medication recommendation.

The diversities of large-scale semistructured data make the extraction of implicit semantic information have enormous difficulties. This paper proposes an automatic and unsupervised method of text categorization, in which tree-shape structures are used to represent semantic knowledge and to explore implicit information by mining hidden structures without cumbersome lexical analysis. Mining implicit frequent structures in trees can discover both direct and indirect semantic relations, which largely enhances the accuracy of matching and classifying texts. The experimental results show that the proposed algorithm remarkably reduces the time and effort spent in training and classifying, which outperforms established competitors in correctness and effectiveness.

In this paper, we design a classifier based-on topic for food complain documents, and take a series of measures to the implementation process. In order to accomplish feature reduction, the filter method named term filtering for independent topic features is proposed to compress each topic feature vector. We introduce the created food ontology as background knowledge and to expand the semantic of complaint documents with the aid of HowNet. So we can supplement effective information and improve the effect of text classification. In addition, we take account of different importance between title and body in the text, considering that title can stand out the topic of text better than the textual body. Consequently, we separately calculate the word frequency which words are in textual title and body. The experiments show that it is necessary to consider the different importance between textual title and body, and we can achieve good feature reduction effect using the proposed filter method, and the classification performance get obvious improvement after the process of term expanding. © 2012 ACADEMY PUBLISHER.

Document clustering is an effective way to detect similar clusters of documents in nature. The objective of this paper is to develop an effective methodology to cluster food complaint documents with the guidance of ontology and fuzzy set theory. Here, the terms in the controlled vocabulary of our food ontology describe the hazards in dairy product and will be used to guide feature selection. Then, the fuzzy compatibility relation will be constructed in k-dimensional semantic space and a fuzzy equivalence relation based on the fuzzy compatibility relation can be obtained. Finally, a suitable ¿-cut value is selected to determine the best number of clusters. Several documents have been tested and the results show our methodology is effective and accurate.

Concurrent with progress in food supervision, an overwhelming number of textual complaints are accumulating in web pages. The representation of these documents by a set of domain-specific terminologies and their relationships is the result of this process. The objective of this paper is to develop an effective method to cluster food complaint documents based on a user-specified ontology. First, we generalize some concepts with a controlled vocabulary in our food ontology based on Hownet to extract the document features. Especially, the food ontology will be extended during the procedure of feature extraction. Term clustering techniques and latent semantic analysis (LSA) are used for document clustering. The experimental results show the effectiveness of the methodology. © 2011 ISSN 2185-2766.

Sequential recommendation systems aim to predict users¿ next actions based on the preferences learned from their historical behaviors. There are still fundamental challenges for sequential recommender. First, with the popularization of online services, recommender needs to serve both the warm- and cold-start users. However, most existing models depending on user-item interactions lose merits due to the difficulty of learning sequential dependencies with limited interactions. Second, users¿ behaviors in their historical sequences are often implicit and complex due to the objective variability of reality and the subjective randomness of users¿ intentions. It is difficult to capture the dynamic transition patterns from these user-item interactions. In this work, we propose a graph-based interpolation enhanced sequential recommender with deformable convolutional network (GISDCN). For cold-start users, we re-construct item sequences into a graph to infer users¿ possible preferences. To capture the complex sequential dependencies, we employ the deformable convolutional network to generate more robust and flexible filters. Finally, we conduct comprehensive experiments and verify the effectiveness of our model. The experimental results demonstrate that GISDCN outperforms most of the state-of-the-art models at cold-start conditions.

The swarm intelligent algorithms (SIs) are effective and widely used, while the balance between exploitation and exploration directly affects the accuracy and efficiency of algorithms. To cope with this issue, a backbone whale optimization algorithm based on cross-stage evolution (BWOACS) is proposed. BWOACS is mainly composed of three parts: (1) adopts the density peak clustering (DPC) method to actively divide the population into several sub-populations, generates the backbone representatives (BR) during backbone construction stage; (2) determines the deviation placement (DP) by constructing the co-evolution operators (CE), the search space expansion operators (SE) and the guided transfer operators (GT) during bionic evolution strategy stage; (3) realises the bionic optimisation through DP during backbone representatives guiding co-evolution stage. To verify the accuracy and performance of BWOACS, we compare BWOACS with other variants on 9 IEEE CEC 2017 benchmark problems. Experimental results indicate that BWOACS has better accuracy and convergence speed than other algorithms.

Most of the existing multi-objective optimization algorithms try to evenly distribute all solutions in the objective space. But for the irregular Pareto front(PF), it is difficult to find the real PF. Aiming at the multi-objective optimization problem with complex PF, a multi-objective evolutionary algorithm for adaptive fitting dominant hyperplane (MOEA_DH ) is developed. Before each iteration, non-dominated sorting is applied on all candidate solutions. Solutions in the first front are used to fit a hyperplane in the objective space, which is called the current dominant hyperplane(DH). DH reflects the evolution trend of the current generation of non-dominanted solutions and guides the rapid convergence of dominanted solutions. A new partial ordering relation determined by front number and crowding distance on DH is set. When solving CF benchmark problems from multi-objective optimization in IEEE Congress on Evolutionary Computation 2019, the experiments validate our advantages to get the PF with better convergence and diversity.

Domain-Specific features clustering aims to cluster the features from related domains into K clusters. Although traditional clustering algorithms can be used to domain-specific features clustering, the performance may not good as the features have little inter-connection in related domains. In this paper, we develop a solution that uses the domain-independent feature as a bridge to connect the domain-specific features. And we use spectral clustering to cluster the domain-specific features into K clusters. We present theoretical analysis to show that our algorithm is able to produce high quality clusters. The experimental results show that our algorithm improves the clustering performance over the traditional algorithms. © 2012 Springer-Verlag.