检索结果-内蒙古大学图书馆

Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients:A Multi-Center Retrospective Study in China

引用

engineering 2022年第1期8卷 116-121页

作者： Ye Yuan Chuan Sun Xiuchuan Tang Cheng Cheng Laurent Mombaerts Maolin Wang Tao Hu Chenyu Sun Yuqi Guo Xiuting Li Hui Xu Tongxin Ren Yang Xiao Yaru Xiao Hongling Zhu Honghan Wu Kezhi Li Chuming Chen Yingxia Liu Zhichao Liang Zhiguo Cao Hai-Tao Zhang Ioannis Ch.Paschaldis Quanying Liu Jorge Goncalves Qiang Zhong Li Yan School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Luxembourg Centre for Systems Biomedicine University of LuxembourgBelval L-4367Luxembourg Department of Plant Sciences University of CambridgeCambridge CB21TNUK Department of Emergency Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China AMITA Health Saint Joseph Hospital Chicago ChicagoIL 60657USA Department of Anesthesiology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Huazhong University of Science and Technology-Wuxi Research Institute Wuxi 214174China Division of Cardiology Department of Internal MedicineTongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Institute of Health Informatics University College LondonLondon NW12DAUK Department of Biomedical Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Infectious Diseases Shenzhen Key Laboratory of Pathogenic Microbiology and ImmunologyNational Clinical Research Center for Infectious DiseaseThe Third People’s Hospital of Shenzhen(Second Hospital Affiliated with the Southern University of Science and Technology)Shenzhen 518055China Department of Electrical and Computer Engineering&Division of Systems Engineering&Department of Biomedical Engineering Boston UniversityBostonMA 02215USA

Coronavirus disease 2019(COVID-19)has become a worldwide *** patients of COVID-19 suffer from a high mortality rate,motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk ***,we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital,Wuhan,China(development cohort)and externally validated with data from two other centers:141 inpatients from Jinyintan Hospital,Wuhan,China(validation cohort 1)and 432 inpatients from The Third People’s Hospital of Shenzhen,Shenzhen,China(validation cohort 2).The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of *** risk score can predict the mortality of individual patients more than 12 d in advance with more than 90%accuracy across all ***,the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low,intermediate,or high risk,with an area under the curve(AUC)score of *** summary,a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2);it has also been validated in independent cohorts.

关键词： COVID-19 Risk score Mortality risk prediction

来源：评论

学校读者我要写书评

暂无评论

Analysis of Determination of Items Ordering Patterns By Using Apriori Method

Analysis of Determination of Items Ordering Patterns By Usin...

引用

International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Bob Subhan Riza Hendra Nusa Putra Ahmad Zamsuri Lusiana Efrizoni Sarjon Defit Information System University of Potensi Utama Medan Indonesia Department Of Medical Record STIKES Dharma Landbouw Padang Indonesia Informatics of Engineering University of Lancang Kuning Pekanbaru Indonesia Informatics of Engineering STMIK Amik Riau Riau Indonesia Computer Science University of Putra Indonesia YPTK Padang Indonesia

ISBN: (纸本)9781665453967

This study is entitled Analysis of the Determination of Ordering Patterns of Items Using Apriori Method Case Study of Universitas Potensi Utama. The research conducted by the author uses data mining and uses the Apriori method which divides arranged items into two so that the more detailed types of items are often ordered so that it can help inventory to increase the stock of items needed by the staff of Universitas Potensi Utama. Ordering items is one of the activities carried out at the Universitas Potensi Utama which is usually used to process work activities in the office. The test results using a priori algorithm and a system built using 2 item sets showed 94% confidence results that have met the needs in determining the pattern of ordering items based on the tendency of ordering items carried out by each department.

关键词： Data mining Intelligent systems Standards Cybernetics

来源：评论

学校读者我要写书评

暂无评论

A Survey of Lung Nodules Detection and Classification from CT Scan Images

引用

computer Systems Science & engineering 2024年第6期48卷 1483-1511页

作者： Salman Ahmed Fazli Subhan Mazliham Mohd Su’ud Muhammad Mansoor Alam Adil Waheed Faculty of Computing and Technology IQRA UniversityIslamabad44000Pakistan Faculty of Engineering and Computer Science National University of Modern LanguagesIslamabad44000Pakistan Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad44000Pakistan Department of Information Sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan

In the contemporary era,the death rate is increasing due to lung ***,technology is continuously enhancing the quality of *** improve the survival rate,radiologists rely on Computed Tomography(CT)scans for early detection and diagnosis of lung *** paper presented a detailed,systematic review of several identification and categorization techniques for lung *** analysis of the report explored the challenges,advancements,and future opinions in computer-aided diagnosis CAD systems for detecting and classifying lung nodules employing the deep learning(DL)*** findings also highlighted the usefulness of DL networks,especially convolutional neural networks(CNNs)in elevating sensitivity,accuracy,and specificity as well as overcoming false positives in the initial stages of lung cancer *** paper further presented the integral nodule classification stage,which stressed the importance of differentiating between benign and malignant nodules for initial cancer ***,the findings presented a comprehensive analysis of multiple techniques and studies for nodule classification,highlighting the evolution of methodologies from conventional machine learning(ML)classifiers to transfer learning and integrated ***,while accepting the strides formed by CAD systems,the review addressed persistent challenges.

关键词： Lung nodules computed tomography scans lung cancer deep learning

来源：评论

学校读者我要写书评

暂无评论

COMPARATIVE ANALYSIS OF STATE-OF-THE-ART DEEP LEARNING MODELS FOR DETECTING COVID-19 LUNG INFECTION FROM CHEST X-RAY IMAGES

arXiv

引用

arXiv 2022年

作者： Ghaffar, Zeba Shah, Pir Masoom Khan, Hikmat Zaidi, Syed Farhan Alam Gani, Abdullah Khan, Izaz Ahmad Shah, Munam Ali Ul Islam, Saif Department of Computer Science Comsats University Islamabad Islamanad Pakistan Department of Computer Science Bacha Khan University KPK Charsadda Pakistan Department of Computer Science and Engineering Chung-Ang University Seoul Korea Republic of Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Faculty of Computing and Informatics University Malaysia Sabah Labuan Malaysia Department of Computer Science Institute of Space Technology Islamabad Pakistan

The ongoing COVID-19 pandemic has already taken millions of lives and damaged economies across the globe. Most COVID-19 deaths and economic losses are reported from densely crowded cities. It is comprehensible that the effective control and prevention of epidemic/pandemic infectious diseases is vital. According to WHO, testing and diagnosis is the best strategy to control pandemics. Scientists worldwide are attempting to develop various innovative and cost-efficient methods to speed up the testing process. This paper comprehensively evaluates the applicability of the recent top ten state-of-the-art Deep Convolutional Neural Networks (CNNs) for automatically detecting COVID-19 infection using chest X-ray images. Moreover, it provides a comparative analysis of these models in terms of accuracy. This study identifies the effective methodologies to control and prevent infectious respiratory diseases. Our trained models have demonstrated outstanding results in classifying the COVID-19 infected chest x-rays. In particular, our trained models MobileNet, EfficentNet, and InceptionV3 achieved a classification average accuracy of 95%, 95%, and 94% test set for COVID-19 class classification, respectively. Thus, it can be beneficial for clinical practitioners and radiologists to speed up the testing, detection, and follow-up of COVID-19 cases. © 2022, CC BY.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Pupillometry examinations of the human eye with the eye diagnostic device PEP-2000-First results

Pupillometry examinations of the human eye with the eye diag...

引用

作者： Schieding, Nikola Reuter, Thomas Grundmann, Andreas Walther, Sebastian Klee, Sascha ICM-Institut Chemnitzer Maschinen-und Anlagenbau E.V. Otto-SchmerbachStr. 19 Chemnitz09117 Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation Tu Ilmenau Ilmenau Germany

Pupillometry forms the diagnostic basis for numerous pathologies of the eye. For this reason, fast and accurate diagnostics in the field of ophthalmology are essential. Two examination techniques, full-field ERG and pupillometry were combined in a diagnostic device developed by ICM e.V. to reduce the examination process for both examiners and patients. In this paper, the device is examined for the quality of the pupillometry measurements. A study with 12 healthy subjects (3 f, 9 m, 36.33 ± 11.94 years) was conducted to evaluate the device. The results showed that the minimal pupil diameter is 40 % higher than the literature values. The main reason for the differences is the low light intensity of 15 cd/m2. However, the maximum pupil diameter is within the range of the researched values. The results of the pupillary reaction measurements show that the values obtained (amplitude, contraction time and peak time) are within the range of literature values. The latency time of 690 ms is 40 % too high. The reason for this could be the moderate pupil detection rate of 50-70 %. Nevertheless, plausible and comparable analysis values could be obtained with the eye diagnostic device PEP-2000. Further work will look at improving pupil detection rates. © 2022 by Walter de Gruyter Berlin/Boston.

关键词： Ophthalmology

来源：评论

学校读者我要写书评

暂无评论

Energy Harvesting Aware Client Selection for Over-the-Air Federated Learning

Energy Harvesting Aware Client Selection for Over-the-Air Fe...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Caijuan Chen Yi-Han Chiang Hai Lin John C.S. Lui Yusheng Ji The Graduate University for Advanced Studies (SOKENDAI) and National Institute of Informatics (NII) Tokyo Japan Department of Electrical and Electronic Systems Engineering Osaka Metropolitan University Osaka Japan Department of Computer Science Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781665435413

Federated learning (FL) has been widely regarded as a promising distributed machine learning technology that utilizes on-device computation while protecting clients' data privacy. To adapt FL to wireless networks, the over-the-air (OTA) computation, which employs the superposition nature of wireless waveforms, can prevent excessive consumption of the communication resources. However, energy harvesting technology can overcome the energy limitation of clients to realize durable computation. Despite the existing works devoted to OTA FL from various aspects, they mostly neglect jointly performing client selection and energy management for energy harvesting devices. In this paper, we investigate the combined problem of client selection and energy management for OTA FL and formulate it as a nonlinear integer programming (NIP) problem to minimize the optimality gap. To solve the NIP problem, we propose a client selection scheme that jointly considers channel state information, residual battery capacities, and dataset size. Our simulation results show that the proposed solution outperforms other comparison schemes within various parameter settings.

关键词： Performance evaluation Integer programming Federated learning Wireless networks Simulation Energy harvesting Global communication

来源：评论

学校读者我要写书评

暂无评论

Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources

arXiv

引用

arXiv 2023年

作者： Rajendran, Suraj Pan, Weishen Sabuncu, Mert R. Chen, Yong Zhou, Jiayu Wang, Fei Tri-Institutional Computational Biology & Medicine Program Cornell University NY United States Division of Health Informatics Department of Population Health Sciences Weill Cornell Medicine New YorkNY United States School of Electrical and Computer Engineering Cornell University IthacaNY United States Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medical School New YorkNY United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States Department of Computer Science and Engineering Michigan State University East LansingMI United States

Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient care, population health, and healthcare providers' workflows. However, the real-world clinical and cost benefits remain limited due to challenges in data privacy, heterogeneous data sources, and the inability to fully leverage multiple data modalities. In this perspective paper, we introduce "patchwork learning" (PL), a novel paradigm that addresses these limitations by integrating information from disparate datasets composed of different data modalities (e.g., clinical free-text, medical images, omics) and distributed across separate and secure sites. PL allows the simultaneous utilization of complementary data sources while preserving data privacy, enabling the development of more holistic and generalizable ML models. We present the concept of patchwork learning and its current implementations in healthcare, exploring the potential opportunities and applicable data sources for addressing various healthcare challenges. PL leverages bridging modalities or overlapping feature spaces across sites to facilitate information sharing and impute missing data, thereby addressing related prediction tasks. We discuss the challenges associated with PL, many of which are shared by federated and multimodal learning, and provide recommendations for future research in this field. By offering a more comprehensive approach to healthcare data integration, patchwork learning has the potential to revolutionize the clinical applicability of ML models. This paradigm promises to strike a balance between personalization and generalizability, ultimately enhancing patient experiences, improving population health, and optimizing healthcare providers' workflows. © 2023, CC BY.

关键词： Data privacy

来源：评论

学校读者我要写书评

暂无评论

Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

引用

Procedia computer Science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

来源：评论

学校读者我要写书评

暂无评论

Monitoring Earth Hazard with Remote Sensing Techniques

引用

Photogrammetric engineering and Remote Sensing 2022年第2期88卷 92-92页

作者： Malarvizhi Kumar, Priyan Afghah, Fatemeh Arunmozhi, Manimuthu Department of Computer Science and Engineering Kyung Hee University Korea Republic of School of Informatics Computing and Cyber Systems Northern Arizona University United States Nanyang Technological University Singapore

来源：评论

学校读者我要写书评

暂无评论

Analytics of ensemble learning-based methods for Arabic text classification

Analytics of ensemble learning-based methods for Arabic text...

引用

International Conference on Intelligent Systems: Theories and Applications (SITA)

作者： Tarik Sabri Mohamed Kissi Said Bahassine Omar El Beggar Informatics Department LIM Laboratory Faculty of Sciences and Techniques of Mohammedia Hassan II University Mohammedia. Casablanca Morocco Department of Computer Science Laboratory of Artificial Intelligence &Complex Systems Engineering (AICSE) National Higher School of Arts and Crafts. Hassan II University Casablanca Morocco

In recent years, the volume of unstructured documents has grown exponentially, necessitating a profound understanding of Machine Learning (ML) methods to accurately classify documents in various domains, including business, media, sports, and more. This study proposes the utilization of ensemble learning methods for the automated classification of new documents. Specifically, ML algorithms and their bagging, boosting, stacking, and voting methods are employed. A comparative analysis of these algorithms is conducted using three benchmark Arabic datasets. The experimental results demonstrate that ensemble learning methods outperform individual learners in terms of accuracy, precision, recall, and F-measure. Among the ensemble learning classifiers, the stacking and voting methods exhibit the most promising performance, achieving the highest accuracy of 95.20% with the ANTCorpus dataset. Additionally, voting has led to improved accuracy of 93.24% for the Khaleej-2004 dataset and 92.15% for the Watan-2004 dataset.

关键词：

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：