检索结果-内蒙古大学图书馆

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Oviya, I.R. Shanmukha Sravya, N. Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350370249

Predicting Protein-protein interactions are central to understanding the intricate mechanisms governing cellular functions, as proteins seldom act in isolation. While current machine learning and deep learning models focus on encoding protein sequences based on residue composition or physicochemical properties, they often overlook the complex relationships between amino acid segments within lengthy sequences. To address these limitations, we propose a novel hybrid model that combines a self-attention to enhance Deep neural network feature extraction with an with FastText Protein embeddings for predicting PPI. Utilizing transfer learning via the unsupervised FastText embedding technique to derive statistical representations of protein sequences, offers advantages over traditional protein encoding techniques in the context of analysing k-Mer sequences. Our model's performance is assessed through 5-fold cross-validation on datasets of *** and Human-Bacillus Anthracis protein interactions, resulting in accuracies of 92.28% and 94.08%, respectively. Compared to existing machine learning models, our model demonstrates superior performance on both datasets. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Generative Artificial Intelligence in Clinical Medicine and Impact on Gastroenterology

引用

Gastroenterology 2025年

作者： Ali Soroush Mauro Giuffrè Sunny Chung Dennis L Shung Division of Data-Driven and Digital Medicine (D3M) Icahn School of Medicine at Mount Sinai Henry D. Janowitz Division of Gastroenterology Icahn School of Medicine at Mount Sinai Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai. Section of Digestive Diseases Department of Medicine Yale School of Medicine. Section of Digestive Diseases Department of Medicine Yale School of Medicine Department of Biomedical Informatics & Data Science Yale School of Medicine. Electronic address: dennis.shung@yale.edu.

The pace of artificial intelligence (AI) integration into healthcare has accelerated with rapid advances in generative AI (GenAI). Gastroenterology and hepatology in particular will be transformed due to the multimodal workflows that integrate endoscopic video, radiologic imaging, tabular data, and unstructured note text. GenAI will impact the entire spectrum of clinical experience, from administrative tasks, diagnostic guidance and treatment recommendations. Unlike traditional machine learning approaches, GenAI is more flexible, with one platform able to be used across multiple tasks. Initial evidence suggests benefit in lower-level administrative tasks such as clinical documentation, medical billing and scheduling, and information tasks such as patient education and summarization of the medical literature. No evidence exists for GenAI solutions for more complex tasks relevant to clinical care, such as clinical reasoning for diagnostic and treatment decisions that may affect patient outcomes. Challenges of output reliability, data privacy, and useful integration remain, with potential solutions include robust validation, regulatory oversight, and "human-AI teaming" strategies to ensure safe, effective deployment. We remain optimistic in the potential of GenAI to augment clinical expertise due to the adaptability of GenAI to handle multiple data modalities to obtain and focus relevant information flows and the human-friendly interfaces that facilitate ease of use. We believe that the potential of GenAI for dynamic human-algorithmic interactions may allow for a degree of clinician-directed customization to enhance human presence.

关键词： AI Safety Gastroenterology Generative Artificial Intelligence Human-AI Collaboration

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence in Gastroenterology and Hepatology: Potential and Perils

引用

Gastroenterology 2025年

作者： Dennis L Shung Marietta Iacucci Section of Digestive Diseases Department of Medicine Department of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT United States. Electronic address: dennis.shung@yale.edu. Microbiome Ireland College of Medicine and Health University College Cork Mercy/Cork University Hospitals Cork Ireland. Electronic address: miacucci@ucc.ie.

来源：评论

学校读者我要写书评

暂无评论

Factors Associated With Influenza Vaccination in a National Veteran Cohort

引用

AJPM Focus 2025年第1期4卷 100290-100290页

作者： Chen, Alissa S. Farmer, Melissa M. Han, Ling Runels, Tessa Bade, Brett Crothers, Kristina Bastian, Lori A. Bazan, Isabel S. Bean-Mayberry, Bevanne A. Brandt, Cynthia A. Akgün, Kathleen M. Section of General Medicine Department of Medicine Yale University School of Medicine New Haven Connecticut United States Center for Study of Healthcare Innovation Implementation and Policy (CSHIIP) VA Greater Los Angeles Healthcare System Los Angeles California United States Pain Research Informatics Multimorbidities and Education (PRIME) Center VA Connecticut Healthcare System West Haven CT United States Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Hempstead New York United States VA Puget Sound Seattle WA United States Division of Pulmonary Critical Care and Sleep Medicine University of Washington Seattle WA United States Pulmonary Critical Care & Sleep Medicine Yale University School of Medicine New Haven Connecticut United States Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles California United States Department of Emergency Medicine Yale University School of Medicine New Haven Connecticut United States Section of Biomedical Informatics and Data Science Yale University School of Medicine New Haven Connecticut United States

Introduction: Only 53% of American adults receive influenza vaccination, and disparities in vaccination exist among particular racial and ethnic groups. This study determines how race, ethnicity, sex, and rurality are associated with influenza vaccination adherence in a national Veteran Health Affairs Administration cohort. Methods: The authors examined differences in documented influenza vaccinations for the 2019–2020 influenza season among Veteran Health Affairs Administration patients in a retrospective cohort study using Veteran Health Affairs Administration administrative electronic health record data. The author used logistic regression to model receipt of influenza vaccination in association with race, ethnicity, sex, and rurality while controlling for clinical diagnoses, demographics, and ambulatory care utilization. The authors also stratified the models by sex and rurality. Results: Among 5,943,918 veterans, 48.6% received influenza vaccination. Unadjusted comparisons showed that those who were vaccinated were more likely to be White, to be of male sex, and to be older. Similar proportions of unvaccinated and unvaccinated veterans were from rural settings. In adjusted models, Black race was most strongly associated with decreased vaccination (AOR=0.69;95% CI=0.69, 0.70), and American Indian/Alaskan Native race also had reduced odds of vaccination (AOR=0.94;95% CI=0.92, 0.95) compared with White race. Female veterans had increased odds of vaccination (AOR=1.20;95% CI=1.19, 1.20) compared with men. Rurality (AOR=0.97;95% CI=0.96, 0.97) was associated with a small decreased odds of vaccination compared with urban. In stratified models, Black veterans were less likely to receive influenza vaccination regardless of sex and rurality than White veterans. American Indian/Alaska Native female veterans had equal odds of vaccination as White female veterans, whereas American Indian/Alaska Native male veterans had reduced odds of vaccination compared with White male v

关键词： Influenza vaccination vaccination coverage vaccine hesitancy veterans

来源：评论

学校读者我要写书评

暂无评论

From Survey to Ontology, the challenges and opportunities in ontologizing questions and answers 13

From Survey to Ontology, the challenges and opportunities in...

引用

13th International Conference on biomedical Ontology, ICBO 2022

作者： Chan, Lauren Phuong, Jimmy Thessen, Anne Hong, Stephanie Essaid, Shahim Haendel, Melissa College of Public Health and Human Sciences Oregon State University CorvallisOR97331 United States Division of Biomedical Health and Informatics University of Washington Medicine SeattleWA98195 United States Department of Biomedical Informatics University of Colorado Anschutz Medical Campus AuroraCO80054 United States Section of Biomedical Informatics and Data Science Johns Hopkins University School of Medicine BaltimoreMD21218 United States

Surveys are an essential tool for gathering data from individuals in a wide variety of fields. Common data Elements (CDEs) are routinely used in surveys to streamline frequently asked questions and support question reuse. While CDEs are a widespread method for collecting survey data, they face a variety of problems including inconsistent file formats, lack of computational encoding, little to no community development standards, and frequent duplication of CDEs across various registries. Alternative approaches such as ontologies can serve as meaningful translation tools to not only support interoperability between similar/duplicate CDEs, but also to coordinate survey data with other ontology aligned data from heterogeneous methodologies including wet lab, clinical, or field research. To capitalize on the extensive data collected using CDEs, we propose a two-step approach of 1) aligning CDEs with ontology terminology to coordinate similar questions based on primary topic and 2) using standardized enumerations for CDE responses that can support data harmonization for meta analytics. Using this approach, greater data integration can support higher powered analytics and coordination of heterogeneous data types which are often difficult to study in tandem. © 2022 Copyright for this paper by its authors.

关键词： Ontology

来源：评论

学校读者我要写书评

暂无评论

Tomato Disease Classification Using CNN 4th

Tomato Disease Classification Using CNN

引用

4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Archanaa, N. Daniel, V. Vifert Jenuben Divya, S. Raja, Kalpana Oviya, I.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Sciences Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States

ISBN: (纸本)9789819736898

Plant disease detection using various machine learning techniques is a very popular research area. Many promising results have been obtained, but only a few specific applications can still make farmers’ lives easier. This research focuses on the important task of identifying tomato diseases at an early stage to prevent their spread and protect crop yields more broadly, emphasizing the idea that disease prevention will more important than treatment. Leveraging the capabilities of convolutional neural networks (CNN), we developed a model that diagnoses eight distinct tomato diseases using only their images. Diseases listed include bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, two-spotted spider mite, target spot, and tomato yellow leaf curl virus. An integral part of our approach involves sophisticated image prepossessing and enhancement techniques, ensuring data quality and model robustness. Feature extraction plays a key role in transforming raw image data into recognizable patterns. The architecture of the CNN has been meticulously designed, consisting of multiple layers with a notable focus on the ReLU activation function and other relevant hyper-parameters. Our main dataset comes from PlantVillage, known for synthesizing rich and diverse plant images. After rigorous training and validation, the CNN model showed exceptional accuracy of 96.88% in tomato disease identification. However, challenges such as overfitting, image quality variability, and class imbalances were encountered but were resolved through strategic data augmentation, careful CNN overlay. In summary, our research presents a practical solution for farmers and agricultural entities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Mapping Study Variables to Common data Elements Using GPT for Sheets: Towards Standardized data Collection and Sharing

Mapping Study Variables to Common Data Elements Using GPT fo...

引用

IEEE International Conference on Healthcare informatics (ICHI)

作者： Pritham Ram Na Hong Hua Xu Xiaoqian Jiang Section of Biomedical Informatics and Data Science School of Medicine Yale University New Haven CT USA McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX USA

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

Secondary use or reuse of biomedical research data has drawn significant attention and is of growing importance. Non-standardized representation and wide variability of clinical and observational data of biomedical research pose a major challenge to data collection, integration, analysis, and sharing. To address this requirement, we present experiences of using GPT for Sheets as a tool to map study-level biomedical research variables or data elements to Common data Elements (CDEs). In this study, the trial variables were extracted from the data dictionaries in the RADx-rad program, and the NIH Pediatric COVID-19 Working Group recommended CDEs that include children (0 – 21 years) were used as the reference CDEs. Mapping experimental variables (vague data elements) to CDEs resulted in an Fl-score of 0.82 when considering only the first recommendation and 0.85 when considering either the first or second recommendation. Our results indicate that GPT language models can differentiate freely defined study variables and map them to standard CDEs. This, in turn, potentially further enhances interoperability with a wider range of biomedical studies.

关键词： Pediatrics Dictionaries Biological system modeling Medical services data collection data models informatics

来源：评论

学校读者我要写书评

暂无评论

GPTON – Generative Pre-trained Transformers enhanced with Ontology Narration for accurate annotation of biological data

arXiv

引用

arXiv 2024年

作者： Li, Rongbin Chen, Wenbo Li, Jinbo Xing, Hanwen Xu, Hua Li, Zhao Zheng, W. Jim McWilliams School of Biomedical Informatics 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

By leveraging GPT-4 for ontology narration, we developed GPTON to infuse structured knowledge into LLMs through verbalized ontology terms, achieving accurate text and ontology annotations for over 68% of gene sets in the top five predictions. Manual evaluations confirm GPTON's robustness, highlighting its potential to harness LLMs and structured knowledge to significantly advance biomedical research beyond gene set annotation. Copyright © 2024, The Authors. All rights reserved.

关键词： Gene Ontology

来源：评论

学校读者我要写书评

暂无评论

Identifying and Extracting Rare Disease Phenotypes with Large Language Models

arXiv

引用

arXiv 2023年

作者： Shyr, Cathy Hu, Yan Harris, Paul Xu, Hua Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN United States School of Biomedical Informatics University of Texas Health Science HoustonTX United States Department of Biomedical Informatics Biostatistics and Biomedical Engineering Vanderbilt University Medical Center NashvilleTN United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States

Rare diseases (RDs) are collectively common and affect 300 million people worldwide. Accurate phenotyping is critical for informing diagnosis and treatment, but RD phenotypes are often embedded in unstructured text and time-consuming to extract manually. While natural language processing (NLP) models can perform named entity recognition (NER) to automate extraction, a major bottleneck is the development of a large, annotated corpus for model training. Recently, prompt learning emerged as an NLP paradigm that can lead to more generalizable results without any (zero-shot) or few labeled samples (few-shot). Despite growing interest in ChatGPT, a revolutionary large language model capable of following complex human prompts and generating high-quality responses, none have studied its NER performance for RDs in the zero- and few-shot settings. To this end, we engineered novel prompts aimed at extracting RD phenotypes and, to the best of our knowledge, are the first the establish a benchmark for evaluating ChatGPT’s performance in these settings. We compared its performance to the traditional fine-tuning approach and conducted an in-depth error analysis. Overall, fine-tuning BioClinicalBERT resulted in higher performance (F1 of 0.689) than ChatGPT (F1 of 0.472 and 0.591 in the zero- and few-shot settings, respectively). Despite this, ChatGPT achieved similar or higher accuracy for certain entities (i.e., rare diseases and signs) in the one-shot setting (F1 of 0.776 and 0.725). This suggests that with appropriate prompt engineering, ChatGPT has the potential to match or outperform fine-tuned language models for certain entity types with just one labeled sample. While the proliferation of large language models may provide opportunities for supporting RD diagnosis and treatment, researchers and clinicians should critically evaluate model outputs and be well-informed of their limitations. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Development of a Natural Language Processing Tool to Extract Acupuncture Point Location Terms

Development of a Natural Language Processing Tool to Extract...

引用

IEEE International Conference on Healthcare informatics (ICHI)

作者： Yiming Li Xueqing Peng Jianfu Li Suyuan Peng Donghong Pei Cui Tao Hua Xu Na Hong School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX USA Section of Biomedical Informatics and Data Science School of Medicine Yale University New Haven CT USA National Institute of Health Data Science Peking University Beijing China The University of Texas MD Anderson Cancer Center Houston TX USA

Acupuncture point location information is significant to the effect of acupuncture therapy. A wealth of knowledge related to acupuncture point locations is typically dispersed across books or reports in free-text format, which can pose challenges for machines to understand and facilitate. In this study, our purpose is to develop a natural language processing (NLP) tool to extract acupuncture point location information. We adopted the World Health Organization (WHO) Standard Acupuncture Point Locations in the Western Pacific Region (WHO Standard) as our reference standard and corpus. Our proposed approach to develop and validate the NLP tool involves three key steps: 1) We leveraged pre-existing terminologies from the anatomy domain, specifically the International Anatomical Terminology (IAT) and Uber-anatomy Ontology (UBERON), to construct a baseline dictionary of acupoint locations. This dictionary was then used in conjunction with a dictionary lookup method to match terms in the acupuncture location descriptions outlined by the WHO Standard; 2) We employed NLP models based on machine learning and deep learning techniques, specifically utilizing Conditional Random Fields (CRFs) and Recurrent Neural Network (RNN) for training. The CRFs and RNN models achieved overall F1 scores of 0.955 and 0.945, respectively; 3) We encoded the extracted entities into standardized concepts and enriched the baseline dictionary using these entities. In this study, a customized NLP tool was developed and validated for extracting acupuncture point location terms. This tool can be used in various informatics applications to support acupuncture research, education, and practices.

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：