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| 1 | Quantifying the effects of long-term news on stock markets on the basis of the multikernel Hawkes process显示文摘Recent studies have revealed that long-term financial news can affect stock markets. However,previous research mainly focuses on modeling the short-term effects of financial news and suffers from the weak ability of quantifying the time-decaying influence of financial news. To fill this gap, this study introduces the Hawkes process to estimate the time-decaying influence of long-term financial news. However, the performance of the conventional Hawkes process is sensitive to the choice of kernel functions. Hence, we propose a novel multikernel-powered Hawkes process framework, which uses multiple kernels to model different time-decaying rates, thus alleviating the instability of our proposed Hawkes process based prediction model.Experimental results show that the proposed framework yields state-of-the-art stock market prediction accuracies on 515 listed companies and gains more profits in market trading simulation compared with baseline methods. News-based stock prediction can complement studies on price-volume-based stock prediction. | Xiao DING Jihao SHI Junwen DUAN Bing QIN Ting LIU | 2021 | Science China(Information Sciences)2021，64，9： | 4 |
| 2 | A Gaussian copula regression model for movie box-office revenues prediction显示文摘In this article, we revisit the task of movie box-office revenues prediction using multi-type features.The movie box-office revenues are affected by numerous factors. Previous work with discriminative models assumes these factors are identically and independently distributed. The correlations between these factors are rarely considered, which limited the performances of discriminative models in this task. To address these problems, we investigate a novel Gaussian copula regression model. Based on this model, we do not need to make any prior assumptions about the marginal distributions of the features. In particular, we perform a cumulative probability estimation on each of the smoothed features. The estimation learns the marginal distributions and maps all features into a uniform vector space. Sequentially, we bridge the marginal distributions with a copula function to create their joint distribution, and learn the dependency structure between them. Moreover, we propose a computational-efficient approximate algorithm for responsible variable inference. Experimental results on two movie datasets from Chinese and U.S. market show that our approach outperforms strong discriminative regression baselines. | Junwen DUAN Xiao DING Ting LIU | 2017 | Science China(Information Sciences)2017，60，9： | 2 |
| 3 | Medical Knowledge Graph:Data Sources,Construction,Reasoning,and Applications显示文摘Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical applications.Thus,understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field.To this end,we offer an in-depth review of MKG in this work.Our research begins with the examination of four types of medical information sources,knowledge graph creation methodologies,and six major themes for MKG development.Furthermore,three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path(RIP)is proposed as a means of expressing the reasoning procedures for MKG.In addition,we explore intelligent medical applications based on RIP and MKG and classify them into nine major types.Finally,we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities. | Xuehong Wu Junwen Duan Yi Pan Min Li | 2023 | Big Data Mining and Analytics2023，6，2： | 1 |
| 4 | Fusion Model for Tentative Diagnosis Inference Based on Clinical Narratives显示文摘In general,physicians make a preliminary diagnosis based on patients’admission narratives and admission conditions,largely depending on their experiences and professional knowledge.An automatic and accurate tentative diagnosis based on clinical narratives would be of great importance to physicians,particularly in the shortage of medical resources.Despite its great value,little work has been conducted on this diagnosis method.Thus,in this study,we propose a fusion model that integrates the semantic and symptom features contained in the clinical text.The semantic features of the input text are initially captured by an attention-based Bidirectional Long Short-Term Memory(BiLSTM)network.The symptom concepts,recognized from the input text,are then vectorized by using the term frequency-inverse document frequency method based on the relations between symptoms and diseases.Finally,two fusion strategies are utilized to recommend the most potential candidate for the international classification of diseases code.Model training and evaluation are performed on a public clinical dataset.The results show that both fusion strategies achieved a promising performance,in which the best performance obtained a top-3 accuracy of 0.7412. | Ying Yu Junwen Duan Min Li | 2023 | Tsinghua Science and Technology2023，28，4： | 0 |
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