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检索条件"机构=Section for Biomedical Informatics and Data Science"
117 条 记 录,以下是41-50 订阅
From reflection to action: Combining machine learning with expert knowledge for nutrition goal recommendations  21
From reflection to action: Combining machine learning with e...
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2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021
作者: Mitchell, Elliot G. Heitkemper, Elizabeth M. Burgermaster, Marissa Levine, Matthew E. Miao, Yishen Hwang, Maria L. Desai, Pooja M. Cassells, Andrea Tobin, Jonathan N. Tabak, Esteban G. Albers, David J. Smaldone, Arlene M. Mamykina, Lena Department of Biomedical Informatics Columbia University United States School of Nursing The University of Texas at Austin United States Department of Population Health Dell Medical School and Department of Nutritional Sciences The University of Texas at Austin United States Department of Computing and Mathematical Sciences California Institute of Technology United States Department of Molecular Cellular and Developmental Biology University of California Santa Barbara United States Department of Science and Math Fashion Institute of Technology United States United States and The Rockefeller University United States Courant Institute of Mathematical Sciences United States University of Colorado Anschutz Medical Campus Section of Informatics and Data Science United States Departments of Pediatrics Biomedical Engineering and Biostatistics and Informatics United States School of Nursing Columbia University United States
Self-tracking can help personalize self-management interventionsfor chronic conditions like type 2 diabetes (T2D), but refecting onpersonal data requires motivation and literacy. Machine learning(ML) methods can ident... 详细信息
来源: 评论
Improving Entity Recognition Using Ensembles of Deep Learning and Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction from Multiple Sources
arXiv
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arXiv 2024年
作者: Li, Yiming Viswaroopan, Deepthi He, William Li, Jianfu Zuo, Xu Xu, Hua Tao, Cui McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Department of Electrical & Computer Engineering Pratt School of Engineering Duke University 305 Tower Engineering Building DurhamNC27708 United States Department of Artificial Intelligence and Informatics Mayo Clinic JacksonvilleFL32224 United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT06510 United States
Objective Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations, identifying potential risks and ensuring the safe use of... 详细信息
来源: 评论
Repurposing Drugs for Alzheimer's Diseases through Link Prediction on biomedical Literature  11
Repurposing Drugs for Alzheimer's Diseases through Link Pred...
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11th IEEE International Conference on Healthcare informatics, ICHI 2023
作者: Xiao, Yongkang Hou, Yu Zhou, Huixue Diallo, Gayo Fiszman, Marcelo Wolfson, Julian Kilicoglu, Halil Chen, You Xu, Hua Mantyh, William G. Zhang, Rui University of Minnesota Institute for Health Informatics MinneapolisMN United States University of Minnesota Department of Surgery MinneapolisMN United States University of Bordeaux Inria Sistm Team AHeaD Inserm 1219 Bordeaux Population Health F-33000 France Pontifical Catholic University of Rio de Janeiro Nites - Núcleo de Inovação e Tecnologia em Saúde Brazil University of Minnesota Division of Biostatistics School of Public Health MinneapolisMN United States University of Illinois Urbana-Champaign School of Information Sciences ChampaignIL United States Vanderbilt University Medical Center Department of Biomedical Informatics NashvilleTN United States Yale University Section of Biomedical Informatics and Data Science New HavenCT United States University of Minnesota Department of Neurology MinneapolisMN United States
Recently, computational drug repurposing has emerged as a promising method for identifying new interventions for diseases. This study predicts novel drugs for Alzheimer's disease (AD) through link prediction on ou... 详细信息
来源: 评论
Delay-induced uncertainty for a paradigmatic glucose-insulin model
arXiv
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arXiv 2020年
作者: Karamched, Bhargav Hripcsak, George Albers, David Ott, William Department of Mathematics Institute of Molecular Biophysics Florida State University TallahasseeFL32306 United States Department of Biomedical Informatics Columbia University New YorkNY10032 United States Section of Informatics and Data Science Department of Pediatrics Department of Biomedical Engineering Department of Biostatistics and Informatics University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Mathematics University of Houston HoustonTX77204 United States
Medical practice in the intensive care unit is based on the assumption that physiological systems such as the human glucose-insulin system are predictable. We demonstrate that delay within the glucose-insulin system c... 详细信息
来源: 评论
Kamino: A Scalable Architecture to Support Medical AI Research Using Large Real World data
Kamino: A Scalable Architecture to Support Medical AI Resear...
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IEEE International Conference on Healthcare informatics (ICHI)
作者: Fongci Lin Patrick Young Huan He Jimin Huang Roger Gagne Daniel Rice Nathan Price Will Byron Yan Hu Donn Felker Will Button Deniella Meeker Allen Hsiao Hua Xu Charles Torre Wade Schulz Section of Biomedical Informatics and Data Science School of Medicine Yale University New Haven CT USA Department of Laboratory Medicine School of Medicine Yale University New Haven CT USA Digital & Technology Solutions Yale New Haven Hospital New Haven CT USA Mcwilliam School of Biomedical Informatics University of Texas Health Science at Houston Houston TX USA Pediatrics and of Emergency Medicine at Yale School of Medicine Yale University New Haven CT USA
Electronic Health Records (EHRs) represent a crucial data source for real-world evidence generation. To facilitate biomedical studies using EHRs, standard data models like the OMOP CDM have been developed. Nevertheles... 详细信息
来源: 评论
Relation Extraction Using Large Language Models: A Case Study on Acupuncture Point Locations
arXiv
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arXiv 2024年
作者: Li, Yiming Peng, Xueqing Li, Jianfu Zuo, Xu Peng, Suyuan Pei, Donghong Tao, Cui Xu, Hua Hong, Na McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT06510 United States Department of Artificial Intelligence and Informatics Mayo Clinic JacksonvilleFL32224 United States Institute of Information on Traditional Chinese Medicine China Academy of Chinese Medical Sciences Beijing100010 China The University of Texas MD Anderson Cancer Center HoustonTX77030 United States
In acupuncture therapy, the accurate location of acupoints is essential for its effectiveness. The advanced language understanding capabilities of large language models (LLMs) like Generative Pre-trained Transformers ... 详细信息
来源: 评论
BiomedRAG: A Retrieval augmented Large Language Model for Biomedicine
arXiv
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arXiv 2024年
作者: Li, Mingchen Kilicoglu, Halil Xu, Hua Zhang, Rui Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States School of Information Sciences University of Illinois at Urbana-Champaign Champaign United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States
Large Language Models (LLMs) have swiftly emerged as vital resources for different applications in the biomedical and healthcare domains;however, these models encounter issues such as generating inaccurate information... 详细信息
来源: 评论
Biomedrag: A Retrieval Augmented Large Language Model for Biomedicine
SSRN
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SSRN 2024年
作者: Li, Mingchen Kilicoglu, Halil Xu, Hua Zhang, Rui Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States School of Information Sciences University of Illinois at Urbana-Champaign Champaign IL United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States
Retrieval-augmented generation (RAG) offers a solution by retrieving knowledge from an established database to avoid hallucination of large language models (LLM). However, these models utilize specialized cross-attent... 详细信息
来源: 评论
Correction: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment
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JMIR Medical Education 2024年 10卷 e57594页
作者: Gilson, Aidan Safranek, Conrad W. Huang, Thomas Socrates, Vimig Chi, Ling Taylor, Richard Andrew Chartash, David Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Department of Emergency Medicine Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States School of Medicine University College Dublin National University of Ireland Dublin Dublin Ireland Section for Biomedical Informatics and Data Science Yale University School of Medicine 300 George Street Suite 501 New Haven 06511 CT United States
In “How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge” (MIR Med Educ 2023;9:e45312) three additions we...
来源: 评论
Analysis of time-to-event for observational studies: Guidance to the use of intensity models
arXiv
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arXiv 2020年
作者: Andersen, Per Kragh Perme, Maja Pohar van Houwelingen, Hans C. Cook, Richard J. Joly, Pierre Martinussen, Torben Taylor, Jeremy M.G. Abrahamowicz, Michal Therneau, Terry M. Section of Biostatistics University of Copenhagen Denmark Department of Biostatistics and Medical Informatics Medical faculty University of Ljubljana Slovenia Department of Biomedical Data Sciences Leiden University Netherlands Department of Statistics and Actuarial Science University of Waterloo Canada Inserm ISPED Bordeaux Populations Health Research Center University of Bordeaux France Department of Biostatistics University of Michigan United States Department of Epidemiology Biostatistics and Occupational Health McGill University Montreal Canada Division of Biomedical Statistics and Informatics Mayo Clinic Rochester United States
This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like t... 详细信息
来源: 评论