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SNER: Semi-Supervised Named Entity Recognition for Large Volume of Diabetes Data

IEEE Journal of Biomedical and Health informatics 2024年第6期29卷 3942-3955页

作者： Jingyi Zuo Qijie Qian Yun Liu Shan Lu Bo Li Yongan Guo Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing University of Posts and Telecommunications Nanjing China School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Department of Information the First Affiliated Hospital Nanjing Medical University Nanjing China Department of Medical Informatics School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Outpatient Department the First Affiliated Hospital Nanjing Medical University Nanjing China College of Arts Business Law Education and Information Technology Victoria University Melbourne VIC Australia

The medical literature and records on diabetes provide crucial resources for diabetes prevention and treatment. However, extracting entities from these textual diabetes data is crucial but challenging. Named entity recognition (NER) - an important corner-stone technology of natural language processing - has been studied well in the general medical field. However, there is still a lack of effective NER methods to handle diabetes data. Briefly, there are three challenges in the real world, including 1) the large volume of diabetes-related data to be processed, 2) the lack of labeled data, and 3) the high costs of manual labeling. To mitigate those challenges, this paper proposes a novel NER method based on semi-supervised learning, namely SNER, for diabetes data processing. It utilizes large amounts of unlabeled data to solve the problem of lack of labeled data. Specifically, it filters the predicted labels based on their confidence and uncertainty scores to reduce the noise entering the model and divide them into positive pseudo-labels and negative pseudo-labels. Also, it utilizes negative pseudo-labels reasonably to improve the training effect of pseudo-labels. Experiments on two public diabetes datasets show that SNER achieves the best performance compared with existing state-of-the-art models.

关键词： Long short term memory Task analysis Diabetes Bioinformatics Training Logic gates Dictionaries

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Benefits and drawbacks of radiofrequency ablation via percutaneous or minimally invasive surgery for treating hepatocellular carcinoma

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World Journal of Gastrointestinal Surgery 2024年第11期16卷 3400-3407页

作者： Ching-Lung Hsieh Cheng-Ming Peng Chun-Wen Chen Chang-Hsien Liu Chih-Tao Teng Yi-Jui Liu Department of Computer Science and Information Engineering Feng Chia UniversityTaichung 40724Taiwan Department of Surgery Chung Shan Medical University HospitalTaichung 40201Taiwan School of Medicine Chung Shan Medical UniversityTaichung 40201Taiwan Department of Radiology Taichung Armed Forces General HospitalTaichung 406Taiwan Department of Medical Imaging and Radiological Sciences Central Taiwan University of Science and TechnologyTaichung 406Taiwan Department of Radiology School of MedicineNational Defense Medical CenterTaipei 11490Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital and China Medical UniversityHsinchu 302Taiwan Institute of Nuclear Engineering and Science National Tsing Hua UniversityHsinchu 300Taiwan Department of Radiology Nantou Hospital of the Ministry of Health and WelfareNantou 540Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia UniversityTaichung 407Taiwan Department of Automatic Control Engineering Feng Chia UniversityTaichung 407Taiwan

The management of early stage hepatocellular carcinoma(HCC)presents significant *** radiofrequency ablation(RFA)has shown safety and effectiveness in treating HCC,with lower mortality rates and shorter hospital stays,its high recurrence rate remains a significant ***,achieving improved survival solely through RFA is challenging,particularly in retrospective studies with inherent *** is commonly used for guiding percutaneous RFA,but its low contrast can lead to missed tumors and the risk of HCC *** enhance the efficiency of ultrasound-guided percutaneous RFA,various techniques such as artificial ascites and contrast-enhanced ultrasound have been developed to facilitate complete tumor *** invasive surgery(MIS)offers advantages over open surgery and has gained traction in various surgical *** studies suggest that laparoscopic intraoperative RFA(IORFA)may be more effective than percutaneous RFA in terms of survival for HCC patients unsuitable for surgery,highlighting its ***,combining MIS-IORFA with these enhanced percutaneous RFA techniques may hold greater significance for HCC treatment using the MIS-IORFA *** article reviews liver resection and RFA in HCC treatment,comparing their merits and proposing a trajectory involving their combination in future therapy.

关键词： Percutaneous radiofrequency ablation Minimally invasive surgery Hepatocellular carcinoma Intraoperative radiofrequency ablation Contrast-enhanced ultrasound

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DenseSplat: Densifying Gaussian Splatting SLAM with Neural Radiance Prior

arXiv

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arXiv 2025年

作者： Li, Mingrui Liu, Shuhong Deng, Tianchen Wang, Hongyu the Department of Computer Science Dalian University of Technology Dalian China the Department of Mechano-informatics Information Science and Technology The University of Tokyo Tokyo Japan Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education China

Gaussian SLAM systems excel in real-time rendering and fine-grained reconstruction compared to NeRF-based systems. However, their reliance on extensive keyframes is impractical for deployment in real-world robotic systems, which typically operate under sparse-view conditions that can result in substantial holes in the map. To address these challenges, we introduce DenseSplat, the first SLAM system that effectively combines the advantages of NeRF and 3DGS. DenseSplat utilizes sparse keyframes and NeRF priors for initializing primitives that densely populate maps and seamlessly fill gaps. It also implements geometry-aware primitive sampling and pruning strategies to manage granularity and enhance rendering efficiency. Moreover, DenseSplat integrates loop closure and bundle adjustment, significantly enhancing frame-to-frame tracking accuracy. Extensive experiments on multiple large-scale datasets demonstrate that DenseSplat achieves superior performance in tracking and mapping compared to current state-of-the-art methods. © 2025, CC BY.

关键词： Real time systems

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The applied principles of EEG analysis methods in neuroscience and clinical neurology

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Military medical Research 2024年第6期11卷 907-941页

作者： Hao Zhang Qing-Qi Zhou He Chen Xiao-Qing Hu Wei-Guang Li Yang Bai Jun-Xia Han Yao Wang Zhen-Hu Liang Dan Chen Feng-Yu Cong Jia-Qing Yan Xiao-Li Li School of Systems Science Beijing Normal UniversityBeijing 100875China College of Electrical and Control Engineering North China University of TechnologyBeijing 100041China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Department of Psychology the State Key Laboratory of Brain and Cognitive Sciencesthe University of Hong KongHong Kong SAR 999077China HKU-Shenzhen Institute of Research and Innovation Shenzhen 518057GuangdongChina Department of Health Technology and Informatics the Hong Kong Polytechnic UniversityHong Kong SAR 999077China Department of Rehabilitation Medicine the First Afliated Hospital of Nanchang UniversityNanchang 330006China Rehabilitation Medicine Clinical Research Center of Jiangxi Province Nanchang 330006China Beijing Key Laboratory of Learning and Cognition School of PsychologyCapital Normal UniversityBeijing 100048China School of Communication Science Beijing Language and Culture UniversityBeijing 100083China Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004HebeiChina School of Computer Science Wuhan UniversityWuhan 430072China School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116081LiaoningChina Guangdong Artifcial Intelligence and Digital Economy Laboratory(Guangzhou) Guangzhou 510335China.

Electroencephalography(EEG)is a non-invasive measurement method for brain *** to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical *** article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological ***,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are ***,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same ***,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are *** article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.

关键词： Electroencephalogram analysis methods Applied principles Neuroscience Diagnosis Neurological diseases

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Intelligent diagnosis of atrial septal defect in children using echocardiography with deep learning

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虚拟现实与智能硬件（中英文） 2024年第3期6卷 217-225页

作者： Yiman LIU Size HOU Xiaoxiang HAN Tongtong LIANG Menghan HU Xin WANG Wei GU Yuqi ZHANG Qingli LI Jiangang CHEN Shanghai Key Laboratory of Multidimensional Information Processing School of Communication&Electronic EngineeringEast China Normal UniversityShanghaiChina Department of Pediatric Cardiology Shanghai Children's Medical CenterSchool of MedicineShanghai Jiao Tong UniversityShanghaiChina Department of Applied Mathematics Xi'an Jiaotong-Liverpool UniversitySchool of ScienceSuzhouChina School of Health Sciences and Engineering University of Shanghai for Science and TechnologyShanghaiChina Minhang District Centers for Disease Control and Prevention ShanghaiChina Faculty of Traditional Chinese Medicine Naval Medical UniversityShanghaiChina Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation Ministry of EducationShanghai 201203China

Background Atrial septal defect(ASD)is one of the most common congenital heart *** diagnosis of ASD via transthoracic echocardiography is subjective and *** The objective of this study was to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic static images using end-to-end convolutional neural *** proposed depthwise separable convolution model identifies ASDs with static color Doppler images in a standard *** the standard views,we selected two echocardiographic views,i.e.,the subcostal sagittal view of the atrium septum and the low parasternal four-chamber *** developed ASD detection system was validated using a training set consisting of 396 echocardiographic images corresponding to 198 ***,an independent test dataset of 112 images corresponding to 56 cases was used,including 101 cases with ASDs and 153 cases with normal *** The average area under the receiver operating characteristic curve,recall,precision,specificity,F1-score,and accuracy of the proposed ASD detection model were 91.99,80.00,82.22,87.50,79.57,and 83.04,*** The proposed model can accurately and automatically identify ASD,providing a strong foundation for the intelligent diagnosis of congenital heart diseases.

关键词： Deep learning Atrial septal defect Echocardiography

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Dermoscopic Image Classification Using Attention Mechanism and Ensemble Learning Approaches

Dermoscopic Image Classification Using Attention Mechanism a...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Huang, Shanchuan Lei, Hongwei Jin, Liuhan Yang, Jinzhu Jiang, Tao Yao, Yudong Grzegorzek, Marcin Li, Chen Northeastern University Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang China Chengdu University of Traditional Chinese Medicine School of Intelligent Medicine Chengdu China Stevens Institute of Technology Department of Electrical and Computer Engineering United States Institute of Medical Informatics University of Luebeck Luebeck Germany

ISBN: (纸本)9798350324457

Background and purpose: Skin tumours have become one of the most common diseases worldwide. While benign ones are not usually a threat to human health, malignant ones can develop into skin cancer and become life-threatening if left untreated. Early detection of the disease is important for the treatment of patients with skin tumours and dermoscopy is the most effective means of diagnosing skin tumours. However, the complexity of skin tumour cells makes the diagnosis somewhat erroneous for doctors. Therefore, a dermoscopic classification network based on deep learning and computer-aided diagnostic techniques is needed to obtain a high diagnostic accuracy rate for skin tumours. Methods: In this paper, Deep-skin, a model for dermoscopic image classification is proposed, which is based on both attention mechanism and ensemble learning. Considering the characteristics of dermoscopic images, embedding different attention mechanisms on top of Inception-V3 has been suggested to obtain more potential features. We then improve the classification performance by late fusion of the different models. To demonstrate the effectiveness of Deep-skin, experiments and evaluations are performed on the publicly available dataset Skin Cancer: Malignant vs. Benign and compare the performance of Deep-skin with other classification models. Results: The experimental results indicate that Deep-skin performs well on the dataset in comparison to other models, achieving a maximum accuracy of 87.8%.Conclusion: In this paper, the Deep-skin model is proposed for the classification of dermoscopic images and has shown better performance. In the future, we intend to investigate better classification models for automatic diagnosis of skin tumours. Such models can potentially assist physicians and patients in clinical settings. © 2023 IEEE.

关键词： Attention mechanism Ensemble learning Image classification Late fusion Skin Cancer

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BCSNP-ML: A Novel Breast Cancer Prediction Model Base on LightGBM and Estrogen Metabolic Enzyme Genes 1

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2nd International Conference on Internet of Things, Communication and Intelligent technology, IoTCIT 2023

作者： Zheng, Tianlei Geng, Shi Yan, Wei Guan, Fengjun Yang, Na Zhao, Lei Zhang, Bei Zhou, Xueyan Cheng, Deqiang School of Information and Control Engineering China University of Mining and Technology Xuzhou China Health Management Center Affiliated Hospital of Xuzhou Medical University Xuzhou China Pharmacy Department Xuzhou Rehabilitation Hospital of Xuzhou Medical University Xuzhou China Department of Pediatrics Affiliated Hospital of Xuzhou Medical University Xuzhou China Department of Obstetrics and Gynecology Xuzhou Central Hospital Xuzhou Clinical School of Xuzhou Medical University Xuzhou China Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy College of Pharmacy Xuzhou Medical University Xuzhou China

ISBN: (数字)9789819727575

ISBN: (纸本)9789819727568

Estrogen-related metabolic enzyme gene polymorphisms have been demonstrated to be linked to breast cancer, and in this paper, a novel noninvasive breast cancer prediction model was developed utilizing machine learning algorithms incorporating estrogen metabolic enzyme gene single nucleotide polymorphisms (SNPs). To precisely forecast the susceptibility to breast cancer,, the coded data of 14 SNPs from enrolled breast patients and normal women were randomly shuffled, with 80% of the data designated as training data, the remaining 20% reserved as the test group to be validated. Single factor analysis was performed to screen independent risk factors, and subsequent application of Breast Cancer with Single Nucleotide Polymorphisms - Machine Learning model (BCSNP-ML) prediction model was completed using Light Gradient Boosting Machine (LightGBM) algorithm. A total of 14 SNPs variables from 280 subjects were utilized in this study. Single factor analysis indicated that a meaningful association between SULT1A1 rs1042028, CYP1A1 rs1048943, CYP1B1 rs1056827, CYP1A1 rs1056836 and the incidence of breast cancer, with 14 variables demonstrates a notable area under the receiver operating characteristic curve (AUROC) of 0.809. The AUROC of the BCSNP-ML model constructed by four variables was 0.831. Additionally, BCSNP-ML is visualized and interpretated in the paper using SHapley Additive exPlanations analysis to further validate that the model exhibits great potential as a robust tool for clinical forecasting of breast cancer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Enzymes

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Challenges in Implementing ICT Rehabilitation Environments in Real Clinical Practice 8

Challenges in Implementing ICT Rehabilitation Environments i...

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8th IEEE International Forum on Research and Technologies for Society and Industry Innovation, RTSI 2024

作者： Tibollo, Valentina Sacchi, Lucia Malovini, Alberto Giardini, Anna D'Addio, Giovanni Lunetta, Christian Istituti Clinici Scientifici Maugeri Irccs Laboratory of Medical Informatics and Artificial Intelligence Pavia Italy University of Pavia Dept of Electrical Computer and Biomedical Engineering Pavia Italy Istituti Clinici Scientifici Maugeri Irccs Information Technology Department Pavia Italy Bioengineering Unit Istituti Clinici Scientifici Maugeri Irccs Pavia Italy Istituti Clinici Scientifici Maugeri Irccs Dept of Rehabilitative Neurology Department Pavia Italy

ISBN: (纸本)9798350362138

Fit for medical Robotics funded under the National Recovery and Resilience Plan, aims to transform current rehabilitation and assistive models for individuals of all ages with impaired motor, sensory, or cognitive functions. The project seeks to achieve this through the implementation of innovative biorobotic technologies and related digital solutions, alongside new continuum of care paradigms. A key element is the full exploitation of information and communication technology (ICT) across all stages of rehabilitation, ranging from prevention to at-home care during the chronic phase. Within this framework, it is crucial to assess the potentialities, challenges, and potential barriers associated with integrating interventions based on advanced technologies into real-world rehabilitation practices. © 2024 IEEE.

关键词： ICT interoperability rehabilitation research data management

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Crucial control measures to contain China's first Delta variant outbreak

引用

National Science Review 2022年第4期9卷 41-50页

作者： Lei Luo Zifeng Yang Jingyi Liang Yu Ma Hui Wang Chitin Hon Mei Jiang Zhengshi Lin Wenda Guan Zhitong Mai Yongming Li Kailin Mai Zhiqi Zeng Chuanmeizi Tu Jian Song Bin Liu Yong Liu Jianfeng He Huiyuan Li Bosheng Li Hang Dong Yutian Miao Shujun Fan Lirui Fan Xingyi Liang Ke Li Chun Chen Huihong Deng Zhicong Yang Nanshan Zhong Institute of Public Health Guangzhou Medical University & Guangzhou Center for Disease Control and Prevention State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory Healththe First Affiliated Hospital of Guangzhou Medical University Guangzhou Laboratory Bio-Island Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases State Key Laboratory of Quality Research in Chinese Medicine Macau Institute for Applied Research in Medicine and HealthMacau University of Science and Technology Macau Institute of Systems Engineering Macau University of Science and Technology Kingmed Virology Diagnostic & Translational Center Guangzhou Kingmed Center for Clinical Laboratory Co.Ltd Guangdong Center for Disease Control and Prevention Department of Urban and Rural Planning Shunde Urban and Rural Planning Information Research Center

The SARS-C oV-2 B.1.617.2（Delta） variant flared up in late May in Guangzhou,*** characteristics of Delta variant were analysed for 153 confirmed cases and two complete transmission chains with seven generations were fully presented.A rapid transmission occurred in five generations within10 *** basic reproduction number（R0） was 3.60（95% confidence interval:2.50-5.30）.After redefining the concept of close contact,the proportion of confirmed cases discovered from close contacts increased from 43% to 100%.With the usage of a yellow health code,the potential exposed individuals were self-motivated to take a nucleic acid test and regained public access with a negative testing *** the massive requirement of screening,novel facilities like makeshift inflatable laboratories were promptly set up as a vital supplement and 17 cases were found,with 1 *** dynamic adjustment of these three interventions resulted in the decline of Rt from 5.00 to 1.00 within 9 *** breaking the transmission chain and eliminating the transmission source through extending the scope of the close-contact tracing,health-code usage and mass testing,the Guangzhou D elta epidemic was effectively contained.

关键词： COVID-19 public health control measure close contact mass testing

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High-Efficiency Photo-Induced Charge Transfer for SERS Sensing in N-Doped 3D-Graphene on Si Heterojunction

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Energy & Environmental Materials 2024年第2期7卷 268-276页

作者： Guanglin Zhang Jie Sun Genwang Wei Shan Zhang Zhengyi He Huijuan Wu Bingkun Wang Siwei Yang Guqiao Ding Zhiduo Liu Zhiwen Jin Caichao Ye Gang Wang Department of Microelectronic Science and Engineering School of Physical Science and TechnologyNingbo UniversityNingbo 315211China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen UniversityShenzhen 518071China Academy for Advanced Interdisciplinary Studies&Department of Materials Science and Engineering Guangdong Provincial Key Laboratory of Computational Science and Material DesignSouthern University of Science and TechnologyShenzhen 518055China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement(MOE)School of PhysicsBeijing Institute of TechnologyBeijing 100081China School of Physical Science and Technology&Key Laboratory for Magnetism and Magnetic Materials of the Ministry of Education Lanzhou UniversityLanzhou 730000China

Nitrogen-doped three-dimensional graphene(N-doped 3D-graphene)is a graphene derivative with excellent adsorption capacity,large specific surface area,high porosity,and optoelectronic ***,N-doped 3D-graphene/Si heterojunctions were grown in situ directly on silicon(Si)substrates via plasma-assisted chemical vapor deposition(PACVD),which is promising for surface-enhanced Raman scattering(SERS)substrates *** analyses of theoretical simulation,incorporating N atoms in 3D-graphene are beneficial to increase the electronic state density of the system and enhance the charge transfer between the substrate and the target *** enhancement of the optical and electric fields benefits from the stronger light-matter interaction improved by the natural nano-resonator structure of N-doped *** as-prepared SERS substrates based on N-doped 3D-graphene/Si heterojunctions achieve ultra-low detection for various molecules:10^(-8)M for methylene blue(MB)and 10^(-9)M for crystal violet(CRV)with rhodamine(R6G)of 10^(10)*** practical detected,10^(-8)M thiram was precisely detected in apple peel *** results indicate that N-doped 3D-graphene/Si heterojunctions based-SERS substrates have promising applications in low-concentration molecular detection and food safety.

关键词： charge transfer DFT simulation N-doped 3D-graphene SERS Thiram detection

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