Motivation
Polypharmacy is the combined use of drugs for the treatment of diseases. However, it often shows a high risk of side effects. Due to unnecessary interactions of combined drugs, the side effects of polypharmacy increase the risk of disease and even lead to death. Thus, obtaining abundant and comprehensive information on the side effects of polypharmacy is a vital task in the healthcare industry. Early traditional methods employed machine learning techniques to predict side effects. However, they often make costly efforts to extract features of drugs for prediction. Later, several methods based on knowledge graphs are proposed. They are reported to outperform traditional methods. However, they still show limited performance by failing to model complex relations of side effects among drugs.
Results
To resolve the above problems, we propose a novel model by further incorporating complex relations of side effects into knowledge graph embeddings. Our model can translate and transmit multidirectional semantics with fewer parameters, leading to better scalability in large-scale knowledge graphs. Experimental evaluation shows that our model outperforms state-of-the-art models in terms of the average area under the ROC and precision-recall curves.
Availability
Code and data are available at: https://github.com/galaxysunwen/MSTE-master
Cigarette online reviews can truly reflect the word-of-mouth of cigarettes, and help cigarette industrial and commercial enterprises to understand consumers’ cigarette use experience and cigarette word-of-mouth dynamics. In order to extract effective consumer experience information from massive online reviews of cigarette consumption, this paper studies the text sentiment analysis of cigarette online reviews. This paper presents a feature fusion model of convolutional neural network and BiLSTM. Experimental results show that the proposed feature fusion model effectively improves the accuracy of text classification. The model can provide new insight for the evaluation of cigarette management, dynamically monitor the change of consumers’ emotion, and grasp the trend of consumers’ emotion in the tobacco market environment in time.
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