检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Kamdar, Maulik R. Hamamsy, Tymor Shelton, Shea Vala, Ayin Eftimov, Tome Zou, James Tamang, Suzanne Biomedical Informatics Training Program Stanford University United States Center for Population Health Sciences Stanford University United States Foundation for Precision Medicine San Francisco United States Department of Biomedical Data Science Stanford University United States

The United States is in the midst of an opioid epidemic with recent estimates indicating that more than 130 people die every day due to drug overdose. The over-prescription and addiction to opioid painkillers, heroin, and synthetic opioids, has led to a public health crisis and created a huge social and economic burden. Statistical learning methods that use data from multiple clinical centers across the US to detect opioid over-prescribing trends and predict possible opioid misuse are required. However, the semantic heterogeneity in the representation of clinical data across different centers makes the development and evaluation of such methods difficult and non-trivial. We create the Opioid Drug Knowledge Graph (ODKG) - a network of opioid-related drugs, active ingredients, formulations, combinations, and brand names. We use the ODKG to normalize drug strings in a clinical data warehouse consisting of patient data from over 400 healthcare facilities in 42 different states. We showcase the use of ODKG to generate summary statistics of opioid prescription trends across US regions. These methods and resources can aid the development of advanced and scalable models to monitor the opioid epidemic and to detect illicit opioid misuse behavior. Our work is relevant to policymakers and pain researchers who wish to systematically assess factors that contribute to opioid over-prescribing and iatrogenic opioid addiction in the US. Copyright © 2019, The Authors. All rights reserved.

关键词： Epidemiology

来源：评论

学校读者我要写书评

暂无评论

Graph Representation Learning in Biomedicine

arXiv

引用

arXiv 2021年

作者： Li, Michelle M. Huang, Kexin Zitnik, Marinka Bioinformatics and Integrative Genomics Program Harvard Medical School BostonMA02115 United States Health Data Science Program Harvard T.H. Chan School of Public Health BostonMA02115 United States Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Broad Institute of MIT and Harvard CambridgeMA02142 United States Harvard Data Science Initiative CambridgeMA02138 United States

Biomedical networks (or graphs) are universal descriptors for systems of interacting elements, from molecular interactions and disease co-morbidity to healthcare systems and scientific knowledge. Advances in artificial intelligence, specifically deep learning, have enabled us to model, analyze, and learn with such networked data. In this review, we put forward an observation that long-standing principles of systems biology and medicine-while often unspoken in machine learning research-provide the conceptual grounding for representation learning on graphs, explain its current successes and limitations, and even inform future advancements. We synthesize a spectrum of algorithmic approaches that, at their core, leverage graph topology to embed networks into compact vector spaces. We also capture the breadth of ways in which representation learning has dramatically improved the state-of-the-art in biomedical machine learning. Exemplary domains covered include identifying variants underlying complex traits, disentangling behaviors of single cells and their effects on health, assisting in diagnosis and treatment of patients, and developing safe and effective medicines. © 2021, CC BY.

关键词： Vector spaces

来源：评论

学校读者我要写书评

暂无评论

Ramadhan short-term electric load: A hybrid model of cycle spinning wavelet and group method data handling (CSW-GMDH)

IAENG International Journal of Computer Science

引用

IAENG International Journal of Computer science 2019年第4期46卷 670页

作者： Caraka, Rezzy Eko Chen, Rung Ching Toharudin, Toni Pardamean, Bens Bakar, Sakhinah Abu Yasin, Hasbi College of Informatics Chaoyang University of Technology Taichung City41349 Taiwan Department of Statistics Padjadjaran University Bandung Indonesia College of Informatics Chaoyang University of Technology Taichung City41349 Indonesia Bioinformatics Data Science Research Center Bina Nusantara University Indonesia BINUS Graduate Program-Master of Computer Science Bina Nusantara University. Indonesia School of Mathematical Sciences FST The National University of Malaysia Malaysia Department of Statistics Diponegoro University Semarang Indonesia

In general, performing a nonlinearity time series analysis in the modeling of data can reach a robust and increase the quality of the results. Wavelet methods have successfully been applied in a great variety of applications for modeling also forecasting. Wavelet Transform divided into two categories. There is continuous wavelet (CWT) and a discrete wavelet transform (DWT). Cycle spinning unlike the discrete wavelet transform (DWT), is highly redundant, non-orthogonal, also defined naturally for all sample sizes. There is a Group Method of data Handling (GMDH) algorithm, which is a multivariate analysis method can be used in modeling and identifying uncertainty on linear also nonlinearity systems. In this paper, we aim to explain the combination of A-Trous wavelet transforms applied on cycle spinning and group method of data handling (GMDH) in data of short-term electric load holy month of Ramadhan from 2014 to 2015. © 2019, International Association of Engineers.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Latent Regression and Ordination Risk of Infectious Disease and Climate

引用

Procedia Computer science 2021年 179卷 25-32页

作者： Rezzy Eko Caraka Rung Ching Chen Youngjo Lee Prana Ugiana Gio Arif Budiarto Bens Pardamean College of Informatics Chaoyang University of Technology 168 Jifong East Road Wufong Dist. Taichung City 41349 Taiwan (R.O.C.). Department of Statistics College of Natural Science Seoul National University Gwanak-gu Seoul 08826 Korea Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan 20222 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Indonesia.

Global warming arising from climate change can increase the spread of deadly diseases. Effort is needed to develop a set of policies for the government to stem or reduce health risks from global warming. The purpose of this paper is to examine more detail and comprehensively about the relationship among climate and event disease count in Taiwan using the partial least square latent regression model. The results obtained that of the 17 types of diseases in Taiwan, that has the most significant loading factor is Amoebiasis, Malaria and Chikungunya. At the same time, climate variables that have the biggest most significant factor are Number day with max temp more than 30, Number day Temp more than 25, and Rainfall PH. Cronbach’s Alpha infectious disease 0.9696 and climate 0.2813. At the same time, the value of Dillon Goldstein’s rho infectious disease 0.974 and climate 0.6404, respectively.

关键词： Disease Counts Climate Latent PLS Regression

来源：评论

学校读者我要写书评

暂无评论

Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective

Computational and Systems Oncology

引用

Computational and Systems Oncology 2023年第3期3卷

作者： Ai, Xusheng Smith, Melissa C Feltus, Frank Alex Electrical and Computer Engineering Department Clemson University Clemson SC United States Department of Genetics and Biochemistry Biomedical Data Science & Informatics Program Clemson Center for Human Genetics Clemson University Clemson SC United States

The remarkable flexibility and adaptability of generative adversarial networks (GANs) have led to the proliferation of its models in bioinformatics research. Proteomic and transcriptomic profiles have been shown to be promising methods for discovering and identifying disease biomarkers. However, those analyses were performed by trained human examiners making the process tedious, time consuming, and hard to standardize. With the development of GANs, it is now possible to reduce computational costs and human time for bioinformatics analysis to produce effective biomarkers. Moreover, GANs help address the lack of phenotypic state transitional gene expression data as well as avoid protected human data constraints by generating RNA sequencing (RNA-seq) data from random vectors. The purpose of this review is to summarize the use of GAN approaches and techniques to augment RNA-seq expression data and identify clinically useful biomarkers. We compare different studies that use different types of GAN models to examine the biomarkers. Also, we identify research gaps and challenges that apply GANs to bio-informatics. Finally, we propose potential directions for future research. © 2023 The Authors. Computational and Systems Oncology published by Wiley Periodicals LLC.

关键词： biomarkers data augmentation generative adversarial network RNA sequencing

来源：评论

学校读者我要写书评

暂无评论

Developmental exposure of California mice to endocrine disrupting chemicals and potential effects on the microbiome-gut-brain axis at adulthood (vol 10, 10902, 2020)

引用

SCIENTIFIC REPORTS 2020年第1期10卷 1-17页

作者： Kaur, Sarabjit Sarma, Saurav J. Marshall, Brittney L. Liu, Yang Kinkade, Jessica A. Bellamy, Madison M. Mao, Jiude Helferich, William G. Schenk, A. Katrin Bivens, Nathan J. Lei, Zhentian Sumner, Lloyd W. Bowden, John A. Koelmel, Jeremy P. Joshi, Trupti Rosenfeld, Cheryl S. Christopher S Bond Life Sciences Center University of Missouri Columbia USA Biomedical Sciences University of Missouri Columbia USA MU Metabolomics Center University of Missouri Columbia USA MU Institute of Data Science and Informatics University of Missouri Columbia USA Food Science and Human Nutrition University of Illinois Urbana USA Physics Randolph College Lynchburg USA DNA Core Facility University of Missouri Columbia USA Department of Biochemistry University of Missouri Columbia USA Department of Physiological Sciences College of Veterinary Medicine University of Florida Gainesville USA Center for Environmental and Human Toxicology College of Veterinary Medicine University of Florida Gainesville USA Environmental Health Sciences Yale University New Haven USA Department of Health Management and Informatics School of Medicine University of Missouri Columbia USA Thompson Center for Autism and Neurobehavioral Disorders University of Missouri Columbia USA Genetics Area Program University of Missouri Columbia USA

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Relative magnetic field and slipping effect on Casson dusty fluid of two phase fluctuating flow over inclined parallel plate

引用

South African Journal of Chemical Engineering 2023年 44卷 135-146页

作者： khan, Dolat Kumam, Poom Kumam, Wiyada Suttiarporn, Panawan Rehman, Ali Fixed Point Research Laboratory Fixed Point Theory and Applications Research Group Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Applied Mathematics for Science and Engineering Research Unit (AMSERU) Program in Applied Statistics Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani 12110 Thailand Faculty of Science Energy and Environment King Mongkut's University of Technology North Bangkok Rayong Campus Rayong 21120 Thailand Special Interest Group for Modelling and Data Analytics Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus 21030 Malaysia

Due to its significant applications in magnetic devices for cell separation, magnetic drugs for cancer tumor treatment, blood flow adjustment during surgery, magnetic endoscopy, and fluid pumping in industrial and engineering processes, magnetohydrodynamics (MHD) has a wide range of applications. Therefore, the aim of this study to investigate the Relative Magnetic Field and slipping effect on Casson dusty fluid of two-phase fluctuating flow over inclined parallel plate. More exactly, the relative magnetic phenomena are fixed relatively to the plate (MFFRP) or fixed relatively to the fluid (MFFRF). The mathematical modeling was established as the set of partial differential equations for dusty Casson fluid. Buckingham's pi theorem is used to find out the dimensionless variables, to make our system dimensionless. The solution in perturb form is to find out by incorporating Poincare–Lighthill perturbation technique (PLPT). To know in-depth different parameters the graphical results for both velocities are plotted with the help of software Mathcad- 15. It is clearly detected that the relative magnetic field performs a vital part in the fluid as well as particle motion. Moreover, the relative magnetic field play a vital role in the decreasing of skin friction of both plates. © 2023

关键词： Brinkman fluid Dust particles Fluctuating vertical plate Inclined relative magnetic field PLPT

来源：评论

学校读者我要写书评

暂无评论

Through the Lens of Patient Partners: Challenges in Accrual of Older Adults to NCI Clinical Trials

引用

Journal of the National Cancer Institute - Monographs 2022年第60期2022卷 125-134页

作者： BrintzenhofeSzoc, Karlynn Canin, Beverly Casas-Silva, Esmeralda Denicoff, Andrea Braun-Inglis, Christa Okado, Izumi Bakos, Alexis Oncology Social Work Kent School of Social Work University of Louisville Louisville KY United States SCOREboard Patient Advocate Board The Cancer and Aging Research Group United States Center for Biomedical Informatics and Information Technology Informatics and Data Science Program National Institutes of Health National Cancer Institute Rockville MD United States Division of Cancer Treatment and Diagnosis Cancer Therapy and Evaluation Program National Institutes of Health National Cancer Institute Rockville MD United States Clinical Faculty UH Nancy Atmospera-Walch School of Nursing University of Hawaii Cancer Center Hawaii M U NCORP Honolulu HI United States University of HawaiiCancer Center Honolulu HI United States Division of Cancer Prevention Community Oncology and Prevention Trials Research Group National Institutes of Health National Cancer Institute Rockville MD United States

The workshop “Engaging Older Adults in Cancer Clinical Trials Conducted in the NCI Clinical Trials Network: Challenges and Opportunities” included a Patient Stakeholder Workgroup that explored the needs and concerns of older adults with cancer regarding clinical trials. To accomplish this, the workgroup conducted patient focus groups in which participants were interviewed, recorded conversations were analyzed and coded, and salient themes were identified. The focus groups identified general barriers to accrual such as complex consent forms, general communication, restrictive eligibility, nonreferrals, patient costs, cultural insensitivity, limited accessibility in community settings, and transportation issues. They also identified the influence of knowledgeable information presenters, improved care, family or caregiver support, and the desire to help others as drivers or reasons to participate in clinical trials. The workshop concluded that multi-level interventions could be used to increase the accrual of older adults to National Cancer Institute clinical trials as well as others. © The Author(s) 2022. Published by Oxford University Press. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Can OpenEHR, ISO 13606 and HL7 FHIR work together? An agnostic perspective for the selection and application of EHR standards from Spain

TechRxiv

引用

TechRxiv 2022年

作者： Pedrera-Jiménez, Miguel Kalra, Dipak Beale, Thomas Carrero, Adolfo Muñoz Balazote, Pablo Serrano Barrio, Noelia García Cano, David Moner Frid, Santiago Tomás, Diego Boscá Segura, Jose Alberto Maldonado Rubí, Raimundo Lozano Data Science Unit Hospital Universitario 12 de Octubre Av. de Córdoba s/n Madrid28041 Spain Data Science Unit Hospital Universitario 12 de Octubre Madrid Spain Veratech for Health Valencia Spain Unit of Medical Informatics Hospital Clinic de Barcelona Barcelona Spain Ars Semantica UK. Specifications Program OpenEHR International United Kingdom Madrid Spain

Due to the heterogeneity of Electronic Health Record (EHR) standards, the decision-making teams, who are not experts in health information, express confusion for selecting and applying these resources in their data platforms. For this reason, a group of experts has analyzed strengths and weaknesses about design, modeling capabilities, flexibility and resources implemented of three relevant standards based on Detailed Clinical Models: OpenEHR, ISO 13606 and HL7 FHIR. Thus, it was concluded that: (1) they are useful for the purposes for which they have been designed and show shortcomings in those for which they have not;(2) they are functionally compatible in health data platforms and methodologies developed in a standards-agnostic perspective;and (3) they are conceptually and technically compatible with each other, so the choice of one or the other does not have a high impact as long as one starts from the one richer in modeling capabilities and flexibility. © 2022, CC BY-NC-SA.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Deep learning-based computational pathology predicts origins for cancers of unknown primary

arXiv

引用

arXiv 2020年

作者： Lu, Ming Y. Zhao, Melissa Shady, Maha Lipkova, Jana Chen, Tiffany Y. Williamson, Drew F.K. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States

Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined1,2. This poses a significant challenge, since modern therapeutics such as chemotherapy regimen and immune checkpoint inhibitors are specific to the primary tumor3. Patients with a CUP diagnosis routinely undergo an extensive diagnostic work-up of pathology, radiology, endoscopy, laboratory tests, and clinical correlation in an attempt to determine the primary origin. Such exploration is not only time and resource consuming, but it might significantly delay administration of the suitable treatment. Despite extensive diagnostic work-ups the primary may never be determined in many cases. Recent work has focused on using genomics and transcriptomics for identification of tumor origins4–6. However, genomic testing is not conducted for every patient and lacks clinical penetration in low resource settings. Herein, to overcome these challenges, we present a deep learning-based computational pathology algorithm-TOAD-that can provide a differential diagnosis for CUP using routinely acquired histology slides. We used 17,486 gigapixel whole slide images with known primaries spread over 18 common origins to train a multi-task deep model to simultaneously identify the tumor as primary or metastatic and predict its site of origin. We tested our model on an internal test set of 4,932 cases with known primaries and achieved a top-1 accuracy of 0.84, a top-3 accuracy of 0.94 while on our external test set of 662 cases from 202 different hospitals, it achieved a top-1 and top-3 accuracy of 0.79 and 0.93 respectively. We further curated a dataset of 717 CUP cases from 151 different medical centers and identified a subset of 290 cases for which a differential diagnosis was assigned. Our model predictions resulted in concordance for 50% of cases (κ=0.4 when adjusted for agreement by chance) and a top-3 agreement of 75%. Our proposed method can be used

关键词： Diseases

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：