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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jing Ma Yu Wang Shuang Ma Jingsong Li Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Engineering Research Center of EMR and Intelligent Expert Systems Ministry of Education Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China

Voriconazole is commonly used as the first-line agent to treat invasive fungal infections (IFIs), but the induction of hepatotoxicity limits its use. To improve the treatment outcomes and minimize toxicity, doctors often administer Therapeutic Drug Monitoring (TDM) to patients receiving voriconazole treatment. Here, we conducted a real-world clinical investigation of voriconazole-treated patients and found significant differences between the TDM (n=318) and non-TDM cohort (n=6,379), and such incidence of hepatotoxicity showed 10.6% in the non-TDM cohort, compared with 21.5% in the TDM cohort. Based on our previous investigation, we presented and compared several machine learning models (including AdaBoost, decision tree, GBDT, logistic regression, neural networks, and random forest) for the early warning of voriconazole-associated hepatoxicity. Through the five-fold cross validation, the logistic model outperformed other models with a mean AUC of 0.7933±0.0934. Our findings offer important insights into the safe and effective application of voriconazole.

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A knowledge-based and data-driven Approach for Predicting Acute Kidney Injury in Patients with Heart Failure*

A Knowledge-based and Data-driven Approach for Predicting Ac...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Shengqiang Chi Tianshu Zhou Weiwei Zhu Xueyao Li Jingsong Li Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Engineering Research Center of EMR and Intelligent Expert Systems Ministry of Education Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China

It has great potential to integrate medical knowledge and electronic health record data for diagnosis prediction. However, present studies only utilized information from knowledge graphs, omitting potentially significant global graph structural features. In this study, we proposed a knowledge and data integrating modeling approach to reconstruct patient electronic health record data with graph structure and use medical knowledge as internal information of patient data to build a risk prediction model for acute kidney injury in patients with heart failure based on graph neural networks. Experimental results based on the MIMIC III data showed that the method proposed was superior to other baseline models in predicting the risk of acute kidney injury in heart failure patients, with an accuracy of 0.725 and an F1 score of 0.755. This study provides a novel approach to the disease risk prediction models that integrates medical knowledge and data.

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Attributes-based person re-identification via CNNs with coupled clusters loss

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Journal of Systems engineering and Electronics 2020年第1期31卷 45-55页

作者： SUN Rui HUANG Qiheng FANGWei ZHANG Xudong Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education) Hefei University of TechnologyHefei 230601China School of Computer and Information Hefei University of TechnologyHefei 230601China

Person re-identification(re-id)involves matching a person across nonoverlapping views,with different poses,illuminations and *** attributes are understandable semantic information to help improve the issues including illumination changes,viewpoint variations and *** paper proposes an end-to-end framework of deep learning for attribute-based person *** the feature representation stage of framework,the improved convolutional neural network(CNN)model is designed to leverage the information contained in automatically detected attributes and learned low-dimensional CNN ***,an attribute classifier is trained on separate data and includes its responses into the training process of our person re-id *** coupled clusters loss function is used in the training stage of the framework,which enhances the discriminability of both types of *** combined features are mapped into the Euclidean *** L2 distance can be used to calculate the distance between any two pedestrians to determine whether they are the *** experiments validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.

关键词： person re-identification(re-id) convolutions neural network(CNN) attributes coupled clusters loss(CCL)

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Convolution Neural Network for Renal Function Assessment Based on Glomerular Filtration Rate

Convolution Neural Network for Renal Function Assessment Bas...

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2021 International Conference on Advanced Technologies and Applications of Modern Industry, ATAMI 2021

作者： Wu, Congzhong Wang, Lulu Yang, Zhen Wang, Jing Han, Li Li, Fei Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei Anhui230601 China School of Computer and Information Hefei University of Technology Hefei Anhui230601 China The Second Hospital of Anhui Medical University Hefei Anhui230601 China

Objective: To develop a Convolution Neural Network (CNN) and discuss its performance in the estimation of Glomerular Fifiltration Rate (GFR) for patients with chronic kidney disease (CKD). Methods: A total of 112 patients with chronic kidney disease were enrolled in this study. The GFR is measured by 99mTc-DTPA renal dynamic and used as standard GFR after normalization by body surface area imaging. We established a CNN model and verified the performance of the model by comparing the GFR predicted by the model with the standard GFR. It turned out that the CNN could better evaluate the GFR of patients which is superior to the CG formula, MDRD formula, CKD-EPI formula and GRNN model. Conclusions: The CNN significantly evaluated GFR for patients with CKD, and it showed better performance than traditional methods and GRNN model and closer to the results of 99mTc-DTPA. The experimental results demonstrated that CNN could be used to estimate GFR. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Neural network models

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RGB-D Visual Odometry Based on Semantic Feature Points in Dynamic Environments 1st

RGB-D Visual Odometry Based on Semantic Feature Points in ...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Wang, Hao Wang, Yincan Fang, Baofu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Anhui230009 China School of Computer Science and Information Engineering HeFei University of Technology Anhui Hefei230009 China

ISBN: (纸本)9783030930486

Various algorithms of traditional visual Simultaneous Localization and Mapping (SLAM) can well match with static scenes, but mismatches will occur in dynamic scenes, which makes the positioning and mapping of the SLAM system produce large errors. Therefore, this paper proposed a visual odometry algorithm based on semantic feature points, which can improve the positioning accuracy in dynamic scenes. The algorithm combined semantic information to detect dynamic objects, and then detects and eliminates dynamic feature points. This paper conducted an extensive evaluation of the system and compared it with ORB-SLAM3 and other dynamic scene SLAM systems. The experimental results show that this method greatly improves the positioning accuracy of the camera and the robustness of the system in a highly complex dynamic environment, which verifies the advancement and effectiveness of the algorithm in this paper. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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An Efficient Customized Blockchain System for Inter-Organizational Processes

An Efficient Customized Blockchain System for Inter-Organiza...

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IEEE International Conference on Web Services (ICWS)

作者： Puwei Wang Zhouxing Sun Rui Li Jinchuan Chen Ping Gong Xiaoyong Du School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering (Ministry of Education) Renmin University of China School of Mathematics Renmin University of China College of Computer and Cyber Security Fujian Normal University

Blockchain technologies pave a promising way for implementing the inter-organizational processes. Most of the current research works translate the execution logic in the process models into the smart contracts, which can run independently on the blockchain without the outside process engine. However, the works usually suffer from the execution and storage costs, since the translation needs to be done when the processes are deployed. In this paper, we customize a process engine for executing the inter-organizational business processes via a blockchain-style procedure, i.e., checking the validity of transactions, adding the valid transactions into the blockchain through the consensus mechanism, and then updating the process states according to the committed transactions. And then, we build a blockchain system by embedding the customized process engine into the blockchain nodes. Moreover, in order to realize the interactions between the inter-organizational processes running on blockchain and the services outside blockchain, we propose a blockchain-based approach for service registration, binding and invocation, and design a lease-based concurrency control protocol to logically isolate transactions from each other when invoking the services simultaneously. Finally, we implement a prototype system based on a permissioned blockchain platform Hyperledger Fabric and a process engine Activiti. The experimental results show the proposed blockchain system can execute the inter-organizational processes correctly and efficiently.

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Mamba Adapter: Efficient Multi-Modal Fusion for Vision-Language Tracking

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Shi, Liangtao Zhong, Bineng Liang, Qihua Hu, Xiantao Mo, Zhiyi Song, Shuxiang Ministry of Education Guangxi Normal University Key Laboratory of Education Blockchain and Intelligent Technology Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Wuzhou University School of Data Science and Software Engineering Wuzhou543002 China

Utilizing the high-level semantic information of language to compensate for the limitations of vision information is a highly regarded approach in single-object tracking. However, most existing vision-language (VL) trackers employ full-parameter fine-tuning, which can easily lead to catastrophic forgetting. Therefore, they fail to fully exploit the prior knowledge of pre-trained models from upstream tasks, resulting in unsatisfactory tracking performance. To alleviate the above problem, we propose a simple yet effective Vision-Language Tracking pipeline based on Mamba Adapter, named MAVLT, which adopts the idea of parameter-efficient fine-tuning (PEFT) to realize the interaction between vision-language modalities. This novel approach offers the following advantages: (1)The knowledge of the upstream pre-trained model is efficiently inherited by freezing its parameters. This ensures that the VL tracking framework only learns the modules for vision and language interaction, with a focus on the fusion between modalities. (2)The modal interaction between language and vision encoders is flexibly bridged in each encoder layer via proposed mamba adapter, enabling efficient interaction of visual and language information at multiple levels. Extensive experiments on five popular vision-language tracking benchmarks validate the effectiveness of the proposed MAVLT. Particularly, the MAVLT achieves 73.4% AUC score on the LaSOT benchmarks with only 0.18%(0.32M) of the total parameters updates. Code and models are available at https://***/GXNU-Zhonglab/MAVLT. © 1991-2012 IEEE.

关键词： Semantics

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A Zero-shot Learning Method with a Multi-Modal knowledge Graph

A Zero-shot Learning Method with a Multi-Modal Knowledge Gra...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuhong Zhang Haitao Shu Chenyang Bu Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Zero-shot learning aims to recognize unseen-classes using some seen-class samples as training set. It is challenging owing to that the feature representations of unseen-class samples are unavailable. Existing methods transfer the mapping from seen-classes to unseen-classes with the correlation as a bridge, in which, the semantic representations are used to discriminate the classes. However, the unavailability of visual representations for unseen-classes and the insufficient discrimination of semantic representations make the zero-shot learning challenging. Therefore, the visual representations are learned as complements to semantic representations to construct a multi-modal knowledge graph (KG), and a zero-shot learning method based on multi-modal KG is proposed in this paper. Specially, a semantic KG is introduced to capture the correlation of classes, and with the correlation, the visual feature representations of all classes are learned. Then, the discriminative visual representations and the semantic representations are used together to construct a multi-modal KG. With the multi-modal KG, the classifier for seen-classes is transferred to unseen classes. Extensive experimental results show the effectiveness of our method.

关键词： Learning systems Representation learning Training Bridges Visualization Correlation Semantics

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xinzheng Li Yuhong Zhang Peipei Li Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Cross-network node classification aims to use a labeled source network to classify nodes in an unlabeled target network. Most of the existing cross-network node classification methods learn the network representations by capturing the node neighborhood and train the classifier on these representations. The performance is highly dependent on the high-quality neighborhood in the network. However, in applications, the degree of nodes generally follows a long-tail distribution, i.e., a significant proportion of nodes are tail nodes with sparse neighborhood. It poses a challenge to existing methods. To this end, a structure similarity graph for cross-network node classitication method (SCNC) is proposed in this paper. Firstly, the potential links between nodes are predicted with the structural similarity metric to construct structure similarity graph, which can enrich the neighborhood of tail nodes. Then, the embedding representations of the structural similarity graph are learned to capture more neighborhood information. Finally, the adversarial is used to learn the domain invariant representations to address cross-network divergence. Extensive experimental results show that our SCNC outperforms the state-of-the-art methods.

关键词： Network topology Tail Learning (artificial intelligence) Prediction algorithms Classification algorithms Noise measurement Task analysis

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Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection 39

Each Fake News is Fake in its Own Way: An Attribution Multi-...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Hao Ma, Zihan Zeng, Zhi Luo, Minnan Zeng, Weixin Tang, Jiuyang Zhao, Xiang Laboratory for Big Data and Decision Nation University of Defense Technology China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China

ISBN: (纸本)157735897X

Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset AMG, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model MGCA to achieve multimodal fake news detection and attribution. Experimental results demonstrate that AMG is a challenging dataset, and its attribution setting opens up new avenues for future research. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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