检索结果-内蒙古大学图书馆

The Internet of Things in Elderly Healthcare Applications: A Systematic Review and Future Directions

IEEE Access 2025年 13卷 71335-71373页

作者： Shirvanian, Naser Shams, Maryam Masoud Rahmani, Amir Lansky, Jan Mildeova, Stanislava Hosseinzadeh, Mehdi Islamic Azad University Department of Computer Engineering Science and Research Branch Tehran14778-93855 Iran Kurdistan University Department of Physics Faculty of Basic Science Sanandaj66177-15175 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliu64002 Taiwan University of Finance and Administration Department of Computer Science and Mathematics Faculty of Economic Studies Prague101 00 Czech Republic Duy Tan University School of Computer Science Da Nang550000 Viet Nam Jadara University Jadara Research Center Irbid21110 Jordan

The increase in the elderly population globally and the prevalence of chronic diseases among the elderly have resulted in rising healthcare costs worldwide. Utilizing the Internet of Things (IoT) and smart sensors to reduce healthcare costs and enhance the quality of life for the elderly is a highly effective and valuable solution. Effective monitoring can improve resource management in IoT-based healthcare systems. We conducted a systematic literature review (SLR) for elderly healthcare on the IoT to evaluate its various facets and key areas. The taxonomy of elderly healthcare studies in the IoT comprises six categories: smart homes, security and privacy, remote healthcare system, wearable devices, smartphone, healthcare and healthcare costs. Management of chronic diseases has the highest percentage of evaluation statistics in the field of smart homes, at 61%. In addition, 26% of smart homes in healthcare are devoted to behavioral characteristics and 13% to biological characteristics. Finally, this study highlights the visions and challenges of elderly healthcare articles and future work. © 2013 IEEE.

关键词： Medical services Internet of Things Older adults Security Monitoring Diseases Costs Privacy Biomedical monitoring Smart homes

来源：评论

学校读者我要写书评

暂无评论

Integrating machine learning and human use experience to identify personalized pharmacotherapy in Traditional Chinese Medicine:a case study on resistant hypertension

引用

Journal of Traditional Chinese Medicine 2025年第1期45卷 192-200页

作者： CHE Qianzi LIU Dasheng XIANG Xinghua TIAN Yaxin XIE Feibiao XU Wenyuan LIU Jian WANG Xuejie WANG Liying BAI Weiguo HAN Xuejie YANG Wei Department of Medical Statistics Institute of Basic Research in Clinical MedicineChina Academy of Chinese Medical SciencesBeijing 100700China Department of Science and Education Medical Statistics Teaching and Research OfficeInstitute of Basic Research in Clinical MedicineChina Academy of Chinese Medical SciencesBeijing 100700China Traditional Chinese Medicine Standards Research Center Medical Statistics Teaching and Research OfficeInstitute of Basic Research in Clinical MedicineChina Academy of Chinese Medical SciencesBeijing 100700China Computer Department Xiyuan Hospital of the China Academy of Chinese Medical SciencesBeijing 100091China

OBJECTIVE:To enhance the understanding of identifying personalized pharmacotherapy options in Traditional Chinese Medicine(TCM),and further support the registration of new TCM ***:Generalized Boosted Models and XGBoost were employed to construct a classification model to identify the bad prognosis factors in resistant hypertension(RH)***,we used association analysis to explore the rules of"symptomsyndrome"and"symptom-herb"for the major influencing factors,in order to summarize prescription pattern and applicable patients of ***:Patients with major adverse cardiac events mostly have complex symptoms of phlegm,stasis,deficiency and fire intermingled with each other,and finally summarized the human experience of using Chinese herbal medicine to precisely intervene in some symptoms of RH patients on the basis of conventional Western medical ***:Machine learning algorithms can make full use of human use experience and evidence to save clinical trial resources and accelerate the development of TCM varieties.

关键词： machine learning human clinical experience personalized pharmacotherapy new drugs registration

来源：评论

学校读者我要写书评

暂无评论

HPML-CVD: Hyperparameter Tuned Machine Learning Model to Predict Cardiovascular Disease

Biomedical Materials and Devices

引用

Biomedical Materials and Devices 2025年 1-15页

作者： Uddin, Nazim Hossain, Md. Sahadat Mahmud, Md. Zulfiker Ferdous, Rebina Talukder, Md. Alamin Department of Information and Communication Technology Chandpur Science and Technology University Chandpur Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Basic and Applied Research on Jute Project Bangladesh Jute Research Institute Dhaka 1207 Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh

Cardiovascular disease (CVD) remains a significant global health concern, necessitating early detection and accurate prediction for effective intervention. Machine learning (ML) offers a data-driven approach to analyzing patient data, identifying complex patterns, and predicting CVD risk factors such as blood pressure, cholesterol, and genetic predispositions. This study employs the Heart Disease UCI dataset, comprising 303 cases and 14 attributes, to develop predictive ML models. A comprehensive feature engineering approach was applied, including Chi-Square feature selection, where the 10 most influential features were identified to enhance model performance. After feature selection, hyperparameter tuning was performed to optimize five classifiers: Random Forest, K-Nearest Neighbors, Decision Tree, Support Vector Machine, and Logistic Regression. The results indicate significant improvements in accuracy after feature engineering and fine-tuning, with the Random Forest classifier achieving the highest accuracy of 93.41%, outperforming previous work on the same dataset by 3.41%, followed by SVM at 91.21%. These findings underscore the potential of ML in early CVD prediction, emphasizing its role in proactive risk management and healthcare. The integration of ML-driven CVD risk assessment paves the way for personalized healthcare, benefiting both patients and medical professionals by facilitating early intervention, improving patient outcomes, and optimizing healthcare resource allocation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Cardiovascular disease Cleveland dataset Heart disease prediction Logistic regression Machine learning

来源：评论

学校读者我要写书评

暂无评论

Domain Aware Multi-task Pretraining of 3D Swin Transformer for T1-Weighted Brain MRI 17th

Domain Aware Multi-task Pretraining of 3D Swin Transformer ...

引用

17th Asian Conference on computer Vision, ACCV 2024

作者： Kim, Jonghun Kim, Mansu Park, Hyunjin Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Center for Neuroscience Imaging Research Institute for Basic Science Suwon Korea Republic of AI Graduate School Gwangju Institute of Science and Technology Gwangju Korea Republic of

ISBN: (纸本)9789819609000

The scarcity of annotated medical images is a major bottleneck in developing learning models for medical image analysis. Hence, recent studies have focused on pretrained models with fewer annotation requirements that can be fine-tuned for various downstream tasks. However, existing approaches are mainly 3D adaptions of 2D approaches ill-suited for 3D medical imaging data. Motivated by this gap, we propose novel domain-aware multi-task learning tasks to pretrain a 3D Swin Transformer for brain magnetic resonance imaging (MRI). Our method considers the domain knowledge in brain MRI by incorporating brain anatomy and morphology as well as standard pretext tasks adapted for 3D imaging in a contrastive learning setting. We pretrain our model using large-scale brain MRI data of 13,687 samples spanning several large-scale databases. Our method outperforms existing supervised and self-supervised methods in three downstream tasks of Alzheimer’s disease classification, Parkinson’s disease classification, and age prediction tasks. The ablation study of the proposed pretext tasks shows the effectiveness of our pretext tasks. Our code is available at ***/jongdory/DAMT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Molecular architecture of mammalian pyruvate dehydrogenase complex

引用

Protein & Cell 2025年第1期16卷 72-78页

作者： Maofei Chen Yutong Song Sensen Zhang Yitang Zhang Xudong Chen Minghui Zhang Meng Han Xin Gao Sai Li Maojun Yang Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological StructureSchool of Life SciencesTsinghua UniversityBeijing 100084China Ministry of Education Key Laboratory of Protein Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Membrane Biology Tsinghua UniversityBeijing 100084China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen University Health Science CenterShenzhen 518060China Protein Research Technology Center Protein Chemistry and Omics PlatformSchool of Life SciencesTsinghua UniversityBeijing 100084China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Cryo-EM Facility Center Southern University of Science and TechnologyShenzhen 518055China Beijing Life Science Academy Beijing 102209China

Dear Editor,Pyruvate dehydrogenase complex(PDHc) is a large multienzyme assembly(Mr = 4–10 million Daltons) consisting of three essential components: pyruvate dehydrogenase(E1p), dihydrolipoyl transacetylase(E2p), and dihydrolipoyl dehydrogenase(E3). These three enzymes perform distinct functions sequentially to catalyze the oxidative decarboxylation of pyruvate with formation of nicotinamide adenine dinucleotide(NADH) and acetyl-coenzyme A(Patel and Roche, 1990).

关键词： sequentially oxidative Dalton

来源：评论

学校读者我要写书评

暂无评论

Construction of Digital Anatomy Experiment Teaching System Based on VR Technology

Construction of Digital Anatomy Experiment Teaching System B...

引用

2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Yu, Penghui Li, Qiao Cai, Shichang Pan, Meisen College of Basic Medicine Hunan University of Medicine Huaihua418000 China College of Computer and Electrical Engineering Hunan University of Arts and Science Hunan Changde415000 China Huaihua Key Laboratory of Digital Anatomy 3D Printing for Clinical Translational Research Hunan Huaihua418000 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Currently, VR technology is constantly developing and widely applied in the field of medical education. Many experiments in medical courses have evolved from simple animated demonstrations to virtual simulation experiments. The current lack of intuitive clarity and ease of operation in teaching human anatomy experiments is a common problem. The 3D anatomical structure teaching model has the advantages of three-dimensional and highly simulated teaching, which can effectively reproduce the three-dimensional structure and three-dimensional entity information of tissues and organs. This article introduces the basic principles of VR technology and its application in anatomy experimental teaching. By comparing the traditional teaching methods and VR integrated teaching methods of 60 students in the anatomy course, the advantages of applying VR anatomical structure teaching mode to human anatomy experimental teaching were analyzed. As a result, the combination of VR anatomical structure teaching and traditional experimental entity specimen teaching solved many difficulties in the teaching process of this specialized course, and gave full play to their respective advantages, effectively overcame the shortcomings of traditional experimental teaching and VR anatomical structure teaching model, and greatly mobilized students' subjective initiative. Make it easier for students to master the complex knowledge of human anatomy. © 2025 SPIE.

关键词： Digital elevation model

来源：评论

学校读者我要写书评

暂无评论

Features Responsible for Spread of Zoonotic Disease Brucella: A Study on Central India Population

引用

SN computer science 2025年第1期6卷 1-19页

作者： Namdev, Nishant Makhijani, Richa Dorshetwar, Kirti Parate, Mayur Shukla, Jayshree L. Husain, Aliabbas A. Kashyap, Rajpal S. Department of Basic Sciences Indian Institute of Information Technology Maharashtra Nagpur India Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Maharashtra Nagpur India Research Centre Central India Institute of Medical Sciences Maharashtra Nagpur India

Zoonotic diseases pose a growing health risk in developing nations because of the ongoing animal-human interaction. India has experienced significant urbanization in recent years, with a large population migration from rural to urban areas. One of the most common and feared risks to which humans are exposed is zoonotic diseases. Numerous studies conducted in India have identified specific zoonoses, such as Listeriosis, Brucellosis, and Bovine tuberculosis, as extremely important to human health. The rough set method is appropriate for several real-world applications and is a component of the computing strategy. Rough sets offer several noteworthy benefits. The rough sets encounter small data sets and provide helpful techniques for revealing hidden patterns in data. In this work, a cross-sectional study was conducted to investigate the prevalence of zoonosis in the Central India Population. Three states were studied for zoonotic infection prevalence, including Maharashtra, Madhya Pradesh, and Chhattisgarh. A total of 7026 samples were recruited by inclusion and exclusion criteria for the study. The lifestyle and animal exposure data of this population was collected and tested for Brucella. On this data, the rough set approach yielded an accuracy of 87.5% for the Brucella-infected case. Our approach has resulted in selecting features responsible for diagnosing the lifestyle parameters which could result in the spread of Brucella. This research also follows the UNSDG goal of good health, essential to sustainable development. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Brucella Feature selection Pattern recognition Rough sets Zoonotic disease

来源：评论

学校读者我要写书评

暂无评论

Characterization and comparative profiling of piRNAs in serum biopsies of pediatric Wilms tumor patients

引用

Cancer Cell International 2025年第1期25卷 1-15页

作者： Mohamed, Fatma S. Jalal, Deena Fadel, Youssef M. El-Mashtoly, Samir F. Khaled, Wael Z. Sayed, Ahmed A. Ghazy, Mohamed A. Biotechnology Program Institute of Basic and Applied Science Egypt-Japan University of Science and Technology Alexandria New Borg El-Arab City Egypt Biochemistry ProgramFaculty of Science Minia University El-Minia Egypt Genomics and Metagenomics Program Department of Basic Research Children’s Cancer Hospital Egypt Cairo 57357 Egypt Bioinformatics Group Center for Informatics Science School of Information Technology and Computer Science Nile University Giza Egypt Leibniz Institute of Photonic Technology Albert Einstein-Straße 9 Jena 07745 Germany Department of Pediatric Oncology National Cancer Institute Cairo Egypt Department of Pediatric Oncology Children’s Cancer Hospital Egypt Cairo 57357 Egypt Consultant Pediatric Oncology Mouwasat Hospital Dammam Saudi Arabia Department of Biochemistry Faculty of Science Ain Shams University Cairo Egypt

Piwi-interacting RNAs (piRNAs) are small non-coding RNAs involved in transposon silencing and linked to cancer progression. However, their role in Wilms tumors (WT) remains unexplored. We conducted a thorough analysis and characterization of piRNAs in serum liquid biopsies of WT patients. Our study examined their expression patterns and functional annotations related to WT pathogenesis, as well as their clinical potential for diagnosis and monitoring. We identified 307 piRNAs expressed in WT serum samples, with 4% classified as repeat-related and 96% as non-repeat-related. The most abundant repeat-related piRNAs originated from LINEs retrotransposon, while tRNA-derived piRNAs were the most prevalent among non-repeat-related piRNAs. Furthermore, a distinct profile of 34 piRNAs showed significant differential expression in WT patients compared to healthy controls—22 downregulated and 12 upregulated. The target genes of differentially expressed piRNAs exhibited significant enrichment in biological pathways related to cytokine activity, inflammatory responses, TGF-beta signaling, p38 MAPK, and ErbB signaling. These genes are also involved in DNA damage response, DNA methylation, cell cycle regulation, as well as kidney development and function. Low expression levels of several piRNAs, especially piR-hsa-1,913,711, piR-hsa-28,190, piR-hsa-28,849, piR-hsa-28,848, and piR-hsa-28,318, showed significant diagnostic potential as non-invasive biomarkers for WT (AUC > 0.8, p < 0.05). Their expression levels also significantly correlated with adverse pathological features, including metastasis, anaplasia, and bilateral WT development. In conclusion, non-transposon-related piRNAs may serve as reliable biomarkers for WT and possess potential non-germline functions, particularly in regulating DNA methylation, cell growth, immune responses, and immune responses. Further studies are warranted to elucidate their functional significance. © The Author(s) 2025.

关键词： Blood-based biomarkers Diagnostic potential Non-germline functions Non-transposon-related PiRNAs PiRNAome profiling Wilms tumor

来源：评论

学校读者我要写书评

暂无评论

On-the-Fly Improving Segment Anything for Medical Image Segmentation using Auxiliary Online Learning

引用

IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Huang, Tianyu Zhou, Tao Xie, Weidi Wang, Shuo Dou, Qi Zhang, Yizhe Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Shanghai Jiaotong University Shanghai China School of Basic Medical Sciences Fudan University Digital Medical Research Center China Shanghai Key Laboratory of Miccai Shanghai China Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong

The current variants of the Segment Anything Model (SAM), which include the original SAM and Medical SAM, still lack the capability to produce sufficiently accurate segmentation for medical images. In medical imaging contexts, it is not uncommon for human experts to rectify segmentations of specific test samples after SAM generates its segmentation predictions. These rectifications typically entail manual or semi-manual corrections employing state-of-the-art annotation tools. Motivated by this process, we introduce a novel approach that leverages the advantages of online machine learning to enhance Segment Anything (SA) during test time. We employ rectified annotations to perform online learning, with the aim of improving the segmentation quality of SA on medical images. To ensure the effectiveness and efficiency of online learning when integrated with large-scale vision models like SAM, we propose a new method called Auxiliary Online Learning (AuxOL), which entails adaptive online-batch and adaptive segmentation fusion. Experiments conducted on eight datasets covering four medical imaging modalities validate the effectiveness of the proposed method. Our work proposes and validates a new, practical, and effective approach for enhancing SA on downstream segmentation tasks (e.g., medical image segmentation). The code is publicly available at https://***/AuxOL/. © 2025 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

IoT in Health Care: Architectural Explorations, Pandemic Perspectives, and Future Horizon 5th

IoT in Health Care: Architectural Explorations, Pandemic Per...

引用

5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Arshi, Oroos Chaudhary, Aryan Chhabra, Gunjan Kaushik, Keshav Singh, Rashmi Bhardwaj, Neha School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India BioTech Sphere Research Kolkata India Department of CSE Graphic Era Hill University Dehradun Dehradun India Graphic Era Deemed to Be University Uttarakhand Dehradun India Amity School of Engineering and Technology Amity University Punjab India Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida India Sharda School of Basic Sciences and Research Sharda University Greater Noida India

ISBN: (纸本)9789819773701

The integration of IoT in health care is a game changer, transforming patient care, clinical workflows, and health management. Its significance stems from the ability to monitor patient vitals in real time, share data seamlessly, and make informed decisions. This work is an attempt to investigate the integration of Internet of Things (IoT) technologies within healthcare systems. It offers insights into its architectural underpinnings, pandemic applications, and future horizons. It explores the essential elements and system architectures specific to healthcare IoT, followed by an examination of current applications and their significance amidst the COVID-19 pandemic. Through a comprehensive review, this paper analyzes the technological landscape of IoT in health care during the pandemic, highlighting key innovations and their implications for patient care. It also provides a case study, including Philips Healthcare’s Care Coordinator, to provide practical insights into IoT-enabled healthcare solutions and their effectiveness. However, the paper also highlights challenges such as privacy concerns and interoperability issues that hinder the widespread adoption of IoT in health care. By providing a multidimensional perspective, this paper contributes to a deeper understanding of IoT’s role in reshaping healthcare delivery, emphasizing its potential to address critical challenges and shape the future of healthcare systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：