检索结果-内蒙古大学图书馆

The Time-Varying Impact of COVID-19 on the Acute Kidney Disorders:A Historical Matched Cohort Study and Mendelian Randomization Analysis

引用

Health data science 2024年第1期4卷 215-225页

作者： Chunyang Li Chao Zhang Jie Chen Yilong Chen Zhiye Ying Yao Hu Huan Song Ping Fu Xiaoxi Zeng Division of Nephrology West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu 610041China Med-X Center for Informatics Sichuan UniversityChengdu 610065China Department of Core Laboratory Sichuan Provincial Peopleʼs HospitalUniversity of Electronic Science and Technology of ChinaChengdu 610054China Centre of Public Health Sciences Faculty of MedicineUniversity of IcelandReykjavíkIceland

Background:This study aimed to explore the time-varying impact of COVID-19 on acute kidney disorders,including acute kidney injury and other acute kidney ***:From the UK Biobank,10,121 participants with COVID-19 were matched with up to 3 historically unexposed controls by age,sex,Townsend deprivation index,and the status of hospitalization or receiving critical *** investigated the association between COVID-19 and incidence of acute kidney disorders,within the ffrst 4 weeks after infection,using conditional and time-varying Cox proportional hazard *** addition,one-sample Mendelian randomization,utilizing the polygenic risk score for COVID-19 as an instrumental variable,was conducted to explore the potential causality of the ***:In the matched cohort study,we observed a signiffcant association between COVID-19 and acute kidney disorders predominantly within the ffrst 3 *** impact of COVID-19 was time dependent,peaking in the second week(hazard ratio,12.77;95%conffdence interval,5.93 to 27.70)and decreasing by the fourth week(hazard ratio,2.28;95%conffdence interval,0.75 to 6.93).In subgroup analyses,only moderate to severe COVID-19 cases were associated with acute worsening of renal function in a time-dependent ***-sample Mendelian randomization analyses further showed that COVID-19 might exert a“short-term”causal effect on the risk of acute kidney disorders,primarily conffned to the ffrst week after ***:The risk of acute kidney disorders following COVID-19 demonstrates a time-varying *** effects were observed only in patients with moderate or severe but not mild COVID-19.

关键词： Kidney matched utilizing

来源：评论

学校读者我要写书评

暂无评论

Enhanced Performance and data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

引用

2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Lung Nodule Analysis in CT Images: Deep Learning for Segmentation and Measurement 24

Lung Nodule Analysis in CT Images: Deep Learning for Segment...

引用

8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Li, Ai-Hsien Adams Aruperes, Cristian Daniel Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Division of Cardiology Far Eastern Memorial Hospital New Taipei TaiwanGraduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanGraduate Institute of Medicine Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400716874

In the realm of research, the global health challenge posed by lung cancer remains pronounced, contributing substantially to annual cancer-related fatalities. The critical imperative lies in the early identification of pulmonary nodules, frequently indicative of impending lung cancer, to enhance patient outcomes and diminish mortality rates. Computed Tomography (CT) imaging stands out as a pivotal diagnostic instrument for the timely detection of these nodules. The swift proliferation of medical imaging data has underscored the pressing necessity for precise and efficient methodologies dedicated to nodule segmentation and measurement. These approaches are crucial in assisting radiologists in their diagnostic and clinical decision-making endeavors. In this study, we introduced a thorough method for analyzing lung nodules, leveraging dataset from Far Eastern Memorial Hospital (FEMH) comprising original CT images and manually annotated ground truth masks obtained with the assistance of radiologists at FEMH. This dataset is utilized for the segmentation of nodules. We employed advanced deep learning models, specifically the U-Net architecture, identified as the optimal model through our training process. We made substantial progress in nodule segmentation, attaining an Intersection over Union (IoU) score of 0.824 and a Dice Coefficient of 0.903 for the FEMH dataset. Furthermore, our performance improved when utilizing the merged dataset comprising FEMH and Luna16, yielding an IoU score of 0.862 and a Dice Coefficient of 0.926. Luna16 has been extensively utilized in numerous studies related to nodule detection and segmentation. In the next phase of the study, the best-performing model from our segmentation phase was utilized to predict nodule masks on the merged dataset. Subsequently, we measured the size of each predicted nodule by comparing it with the size ground truth mask in millimeters. In detail, this study achieved the Pearson Correlation Coefficient (PCC) at 0.

关键词： Computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Physical activity exercise behavior in individuals with a history of stroke: a retrospective study

引用

Discover Medicine 2025年第1期2卷 1-7页

作者： Arora, Shiv Ayyathan, Smriti Sudhir Burzynski, Emily Phillips, Vidith Department of Neurology Sardar Patel Medical College Bikaner India Department of Neurology Brihanmumbai Municipal Corporation (BMC) Mumbai India Department of Neurology Medical University of Warsaw Warsaw Poland Department of Internal Medicine Division of Biomedical Informatics and Data Science School of Medicine Johns Hopkins University Baltimore USA

Stroke remains a significant public health challenge in the United States, impacting the physical capabilities and quality of life of affected individuals. This study assesses the prevalence of self-reported physical activity among stroke patients in relation to demographic, socioeconomic, and healthcare access factors. To examine the levels of physical activity among individuals with a history of stroke and identify the variables influencing these levels. Utilizing the Behavioral Risk Factor Surveillance System (BRFSS) database for 2021, this retrospective study analyzed responses from participants identified with a stroke history. data were assessed using the BRFSS Web Enabled Analysis Tool, focusing on self-reported physical activity and including variables such as age, gender, race, education level, employment status, income, and timing of last routine checkup. Of 17,158 respondents reporting a stroke history, 58.5% engaged in physical activity. Physical activity rates were higher among younger individuals, males, and those with higher socioeconomic status. Physical activity in past one month is significantly associated with a lower prevalence of stroke in age groups 25–44 years, 45–64 years, and 65 + years. Younger individuals (18–24 years) do not show a significant association between physical activity and stroke incidence. While a considerable percentage of stroke patients reported engaging in physical activity, disparities exist influenced by age, gender, and socioeconomic factors. These findings highlight the need for targeted interventions to promote physical activities among stroke survivors, particularly those in lower socioeconomic groups or older age brackets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Technical light-field setup for 3D imaging of the human nerve head validated with an eye model

引用

Current Directions in biomedical Engineering 2021年第2期7卷 433-436页

作者： Schramm, Stefan Dietzel, Alexander Blum, Maren-Christina Link, Dietmar Klee, Sascha Institute of Biomedical Engineering and Informatics Faculty of Computer Sciences and Automation Technische Universität Ilmenau Ilmenau Germany Department of General Health Studies Division Biostatistics and Data Science Karl Landsteiner University of Health Sciences Krems an der Donau Austria

With the new technology of 3D light field (LF) imaging, fundus photography can be expanded to provide depth information. This increases the diagnostic possibilities and additionally improves image quality by digitally refocusing. To provide depth information in the human optic nerve head such as in glaucoma diagnostics, a mydriatic fundus camera was upgraded with an LF imager. The aim of the study presented here was the validation of the technical setup and resulting depth estimations with an appropriate eye model. The technical setup consisted of a mydriatic fundus camera (FF450, Carl Zeiss Meditec AG, Jena, Germany) and an LF imager (R12, Raytrix GmbH, Kiel, Germany). The field of view was set to 30°. The eye model (24.65 mm total length) consisted of a two-lens optical system and interchangeable fundus models with papilla excavations from 0.2 to 1 mm in steps of 0.2 mm. They were coated with red acrylic lacquer and vessels were drawn with a thin brush. 15 images were taken for each papilla depth illuminated with green light (wavelength 520 nm ± 20 nm). Papilla depth was measured from the papilla ground to the surrounding flat region. All 15 measurements for each papilla depth were averaged and compared to the printed depth. It was possible to perform 3D fundus imaging in an eye model by means of a novel LF-based optical setup. All LF images could be digitally refocused subsequently. Depth estimation in the eye model was successfully performed over a 30° field of view. The measured virtual depth and the printed model papilla depth is linear correlated. The presented LF setup allowed high-quality 3D one-shot imaging and depth estimation of the optic nerve head in an eye model. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Optical systems

来源：评论

学校读者我要写书评

暂无评论

Full-field electroretinography examinations of the human eye with the eye diagnostic device PEP-2000-First results

引用

Current Directions in biomedical Engineering 2022年第2期8卷 636-639页

作者： Schieding, Nikola Reuter, Thomas Grundmann, Andreas Walther, Sebastian Klee, Sascha ICM-Institut Chemnitzer Maschinen-und Anlagenbau E.V. Otto-Schmerbach-Str. 19 Chemnitz 09117 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

Full-field electroretinography (full-field ERG) 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 full-field ERG measurements. A feasibility 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 peak times for both light-and dark-adapted measurements were within the range of the researched literature values. However, the amplitudes were markedly lower in both measurements compared to the averaged literature values (dark-adapted about 8.5-fold and light-adapted about 5.5-fold) and are clearly outside the range of values researched. The main reason for this is the use of cup electrodes, which were placed on the skin of the lower eyelid. Nevertheless, plausible and comparable analysis values could be obtained with the eye diagnostic device PEP-2000. Further studies with wire electrodes will be performed. © 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

Are dogs not susceptible to retroviral infections?

引用

Animal Diseases 2024年第3期4卷 217-223页

作者： Jorge Casseb João Henrique Campos Luciano Rodrigo Lopes Laboratory of Investigation in Dermatology and Immunodefciencies Department of DermatologyUniversity of São Paulo Medical SchoolSão PauloSPBrazil Igen-Institute of Immunogenetics São PauloSPBrazil Bioinformatics and Biomedical Data Science Division Health Informatics DepartmentUniversidade Federal de São Paulo–UNIFESP.Rua Botucatu 862Prédio Leal PradotérreoVila ClementinoCEP 04023-062São PauloSPBrazil

Retroviruses have been proven to cause infections and diseases in a series of mammalian hosts but not in ***,this letter discussed the dog susceptibility to retrovirus infection,encompassing arguments to understand why dogs may have not been infected by retroviruses thus *** potential resistance of retrovirus in dogs enables this provocative short communication to discuss this question,looking at some evolutive *** lineage of canids has shown,throughout its evolutionary history,a smaller accumulation of retroviruses in canid genomes,classifed as endogenous *** this context,the genomes of canids seem to ofer obstacles,which have been evolutionarily conserved,in the face of retroviral infection.

关键词： Retrovirus dogs Endogenous retrovirus

来源：评论

学校读者我要写书评

暂无评论

DSLA 2023 Facing the dangers of data illiteracy

引用

Significance 2023年第5期20卷 26-29页

作者： Gascon, Gregg M. Wallman, Katherine K. Ravula, Sridhar Cappelleri, Joseph C. Lee, Julia Lum, Kristian Nagaraja, Chaitra H. Paul, Sudeshna Roy, Dooti Wu, Edward The Department of Biomedical Informatics The Ohio State University College of Medicine United States United States The Human Relations Area Files The Yale University United States The Statistical Research and Data Science Center Pfizer Inc United States The Division of Biostatistics Department of Preventive Medicine the Feinberg School of Medicine Northwestern University United States The Data Science Institute University of Chicago United States The Department of Mathematics and Statistics University of Exeter United Kingdom The School of Nursing Emory University United States Boehringer Ingelheim Inc. United States The Social and Decision Analytics Division The Biocomplexity Institute University of Virginia United States

The data science and Literacy Act (DSLA) of 2023 aims to boost data literacy and data science education across the USA. Gregg M. Gascon, Katherine K. Wallman, Sridhar Ravula, Joseph Cappelleri, Julia Lee, Kristian Lum, Chaitra H. Nagaraja, Sudeshna Paul, Dooti Roy and Edward Wu explain why this opportunity demands our attention © 2023 Blackwell Publishing Ltd. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

powerROC: An Interactive Web Tool for Sample Size Calculation in Assessing Models' Discriminative Abilities

arXiv

引用

arXiv 2025年

作者： Grolleau, François Tibshirani, Robert Chen, Jonathan H. Stanford Center for Biomedical Informatics Research Stanford University United States Department of Statistics Stanford University United States Department of Biomedical Data Science Stanford University United States Stanford Clinical Excellence Research Center Stanford University United States Division of Hospital Medicine Stanford University United States

Rigorous external validation is crucial for assessing the generalizability of prediction models, particularly by evaluating their discrimination (AUROC) on new data. This often involves comparing a new model's AUROC to that of an established reference model. However, many studies rely on arbitrary rules of thumb for sample size calculations, often resulting in underpowered analyses and unreliable conclusions. This paper reviews crucial concepts for accurate sample size determination in AUROC-based external validation studies, making the theory and practice more accessible to researchers and clinicians. We introduce powerROC, an open-source web tool designed to simplify these calculations, enabling both the evaluation of a single model and the comparison of two models. The tool offers guidance on selecting target precision levels and employs flexible approaches, leveraging either pilot data or user-defined probability distributions. We illustrate powerROC's utility through a case study on hospital mortality prediction using the MIMIC database. Copyright © 2025, The Authors. All rights reserved.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Predicting positive Clostridioides difficile test results using large-scale longitudinal data of demographics and medication history

引用

Heliyon 2025年第1期11卷 e41350页

作者： Pham, Anh El-Kareh, Robert Myers, Frank Ohno-Machado, Lucila Kuo, Tsung-Ting Division of Biomedical Informatics Department of Medicine University of California San Diego La Jolla CA United States UCSD Health System San Diego CA United States Department of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT United States Department of Surgery Yale School of Medicine New Haven CT United States

Background: Clostridioides difficile infection is a major health threat. Healthcare institutions have strong medical and financial incentives to keep infections under control. Blanket testing at admission is in general not recommended, and current predictive models either used moderate sample sizes, over-inflated the number of covariates, or chose non-interpretable algorithms. We aim to develop models using patient data to predict positive Clostridioides difficile test results with discrimination performance, interpretable results, and a reasonable number of covariates that reflect health over a long-time span. Materials and methods: We processed records from 157,493 University of California San Diego Health patients seen between January 01, 2016–July 03, 2019 with at least 6 months of medication history, excluding pregnant women, patients under 18, and prisoners. Three models (Logistic Regression, Random Forest, and Ensemble) were constructed using hyper-parameters selected through 10-fold cross-validation. Model performance was measured by the Area Under the Receiver Operating Characteristic Curve (AUROC). The model coefficients’ odds ratios and p-values were calculated for the Logistic Regression model, as were Gini indices for Random Forest. Decision boundary analysis was conducted using pair-wise false positive and false negative cases each model would predict at a specific threshold. Results: Logistic Regression, Random Forest, and Ensemble models yielded test AUROCs of 0.839, 0.851, and 0.866, respectively. Significant covariates that may affect risk include age, immuno-compromised treatments, past antibiotic uses, and some medications for the gastrointestinal tract. Conclusions: The models achieve high discrimination performance (AUROC >0.83). There is a general consensus among different analysis approaches regarding predictors that impact patients' chances of having a positive test, which may influence Clostridioides difficile risk, including features clini

关键词： Clostridioides difficile infection Decision support systems Electronic health record Machine learning

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：