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fusion Model for Tentative Diagnosis Inference Based on Clinical Narratives

清华大学学报:自然科学版(英文版) 2023年第4期28卷 686-695页

作者： Ying Yu Junwen Duan Min Li School of Computer Science and Engineering Central South UniversityChangsha 410083Chinaand also with the School of Computer ScienceUniversity of South ChinaHengyang 421001China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

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.

关键词： tentative diagnosis clinical narrative Bidirectional Long Short-Term Memory(BiLSTM) Term FrequencyInverse Document Frequency(TF-IDF) fusion strategy

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A Novel fusion Forecast Model for Hail Weather in Plateau Areas Based on Machine Learning

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Journal of Meteorological Research 2021年第5期35卷 896-910页

作者： Yan ZHANG Zhong JI Bing XUE Ping WANG School of Electrical and Information Engineering Tianjin UniversityTianjin 300072 Public Meteorological Service Center China Meteorological AdministrationBeijing 100081

In order to improve the accuracy of hail forecasting for mountainous and plateau areas in China,this study presents a novel fusion forecast model based on machine learning techniques.Specifically,known mechanisms of hail formation and two newly proposed elevation features calculated from radar data,sounding data,automatic station data,and terrain data,are firstly combined,from which a hail/short-duration heavy rainfall(SDHR)classification model based on the random forest(RF)algorithm is built up.Then,we construct a hail/SDHR probability identification(PI)model based on the Bayesian minimum error decision and principal component analysis methods.Finally,an"and"fusion strategy for coupling the RF and PI models is proposed.In addition to the mechanism features,the new elevation features improve the models’performance significantly.Experimental results show that the fusion strategy is particularly notable for reducing the number of false alarms on the premise of ensuring the hit rate.A comparison with two classical hail indexes shows that our proposed algorithm has a higher forecasting accuracy for hail in mountainous and plateau areas.All 19 hail cases used for testing could be identified,and our algorithm is able to provide an early warning for 89.5%(17 cases)of hail cases,among which 52.6%(10 cases)receive an early warning of more than 42 minutes in advance.The PI model sheds new light on using Bayesian classification approaches for highdimensional solutions.

关键词： hail identification hail warning random forest Bayesian minimum error decision fusion strategy

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A Novel Approach of Hybrid Genetic Optimization with Elite Ant Colony:Application in the Modulation Recognition

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Wuhan University Journal of Natural Sciences 2011年第1期16卷 43-48页

作者： LIU Shu WANG Hongyuan LANG Liang Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan 430074Hubei China

A novel approach （HGO-EAC） for hybrid genetic op-timization （GO） with elite ant colony （EAC） is proposed for the automatic modulation recognition of communication signals,through which we improve the basic ant colony algorithms by referencing elite strategy and present a new fusion strategy for genetic optimization and elite ant colony. This approach is used to train the neural networks as the classifier for modulation. Simula-tion results indicate good performance on an additive white Gaus-sian noise （AWGN） channel,with recognition rate reaching to 70% especially for CW even at signal-to-noise ratios as low as 5 dB. This approach can achieve a high recognition rate for the typical modulations such as CW,4ASK,4FSK,BPSK,and QAM16. Test result shows that it has better performance than BP algorithm and basic ant colony algorithms by achieving faster training and stronger robustness.

关键词： elite ant colony fusion strategy genetic optimization neural networks classification performance modulation recognition

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Multi-Sensor Image fusion: A Survey of the State of the Art

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Journal of Computer and Communications 2021年第6期9卷 73-108页

作者： Bing Li Yong Xian Daqiao Zhang Juan Su Xiaoxiang Hu Weilin Guo Xi’an High-Tech Research Institute Xi’an China School of Automation Northwestern Polytechnical University Xi’an China Xi’an Satellite Control Center Xi’an China

Image fusion has been developing into an important area of research. In remote sensing, the use of the same image sensor in different working modes, or different image sensors, can provide reinforcing or complementary information. Therefore, it is highly valuable to fuse outputs from multiple sensors (or the same sensor in different working modes) to improve the overall performance of the remote images, which are very useful for human visual perception and image processing task. Accordingly, in this paper, we first provide a comprehensive survey of the state of the art of multi-sensor image fusion methods in terms of three aspects: pixel-level fusion, feature-level fusion and decision-level fusion. An overview of existing fusion strategies is then introduced, after which the existing fusion quality measures are summarized. Finally, this review analyzes the development trends in fusion algorithms that may attract researchers to further explore the research in this field.

关键词： Multi-Sensor Image fusion fusion strategy Feature Enhancement fusion Performance Assessment

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A Real-time RGB-D tracker based on KCF

A Real-time RGB-D tracker based on KCF

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第30届中国控制与决策会议

作者： Han Zhang Meng Cai Jianxun Li Department of Automation Shanghai Jiao Tong University Luoyang Institute of Electro-optical Equipment AVIC

RGB-D tracking has attracted attentions of researchers in computer vision community, there is still a need to boost processing rate. We propose a RGB-D tracker based on color-only KCF which is able to handle the scale changes and occlusions. The proposed tracker fuses the response maps instead of the features to boost both the accuracy and processing rate. Meanwhile, depth data is utilized to form an indicator which is able to detect occlusions and reappearance of target object effectively. The experimental results evaluated on the public Princeton dataset demonstrate that the proposed tracker achieves state-of-the-art performance in accuracy while runs at speed exceeding 47 frames per second.

关键词： RGB-D dataset Visual tracking Kernelized correlation filters fusion strategy Occlusion detection

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