检索结果-内蒙古大学图书馆

Towards Smarter Health Care: Can Artificial Intelligence Help?

CEUR Workshop Proceedings 2021年 3060卷

作者： Lucas, Peter J. F. Stella, Fabio A. Department of Data Science Faculty of EEMCS University of Twente Enschede Netherlands Department of Informatics Systems and Communication University of Milan-Bicocca Milan Italy

来源：评论

学校读者我要写书评

暂无评论

BLE-based Real-time Location System Integration with Hospital Information System to Reduce Patient Waiting Time 3

BLE-based Real-time Location System Integration with Hospita...

引用

3rd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2021

作者： Osman, Mohd Shafarudin Azizan, Azizul Hassan, Khairul Nizam Ghani, Hadhrami Ab. Hassan, Noor Hafizah Yakub, Fitri Daud, Salwani Mohd Latiff, Liza Abdul Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Advanced Informatic Department Kuala Lumpur Malaysia Hospital Putrajaya Emergency and Trauma Department Putrajaya Malaysia Universiti Malaysia Kelantan Institute for Artificial Intelligence Big Data Department of Data Science Kelantan Malaysia Universiti Teknologi Malaysia Malaysia-Japan International Institute of Technology Electronic System Engineering Department Kuala Lumpur Malaysia Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Engineering Department Kuala Lumpur Malaysia

ISBN: (纸本)9781665438971

Emergency and Trauma department (ETD) in Malaysia's public hospitals receives millions of patients than other clinics due to cheap charges and reliable services, however most of them are Green Zone non-critical patients. Long waiting time for patient treatment results into congestion and affects the quality of service provided in the ETD. To date only 20 public hospitals in Malaysia implements Hospital Information System (HIS) and patient waiting time is captured from the system manually, thus lacking the functionality to track patient in realtime. Despite studies conducted worldwide, studies on design, development and implementation of RTLS in public hospital in Malaysia have not been conducted yet. This paper examines a Bluetooth Low Energy (BLE) based Real-time Location System (RTLS) implementation in ETD with HIS integration to reduce patient waiting time. We develop a Bluetooth Low Energy (BLE) based RTLS prototype that is integrated with existing HIS at ETD, Hospital Putrajaya and augment data visualization interventions to reduce patient waiting time in ETD. Preliminary results show substantial benefits of patient waiting time reduction leading to improvement of service quality. Our preliminary findings show when RTLS is introduced in the ETD workflow, the mean ATC time was significantly reduced to 10.47% in comparison to HIS without RTLS. © 2021 IEEE.

关键词： Bluetooth

来源：评论

学校读者我要写书评

暂无评论

Non-invasive brain stimulation reorganises effective connectivity during a working memory task in individuals with Neurofibromatosis Type 1

引用

NeuroImage: Reports 2025年第2期5卷 100258-100258页

作者： Litwińczuk, Marta Czime Garg, Shruti Williams, Stephen R. Green, Jonathan Lea-Carnall, Caroline Trujillo-Barreto, Nelson J. School of Health Sciences University of Manchester Manchester United Kingdom Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom Geoffrey Jefferson Brain Research Centre Manchester Academic Health Science Centre Manchester United Kingdom School of Psychology Manchester Metropolitan University Manchester United Kingdom

Introduction: In a previous study, we examined the effect of atDCS on working memory task performance and modulation of the inhibitory neurotransmitter, gamma-aminobutyric acid (GABA), in the dorsolateral prefrontal cortex (dlPFC). The present study investigates whether tDCS modulates effective connectivity during the task, specifically assessing whether tDCS alters interactions between neuronal populations. Methods: Eighteen adolescents with Neurofibromatosis Type 1 (NF1) completed a single-blind sham-controlled cross-over randomised tDCS trial (with the anode at F3 and cathode at Cz). Dynamic causal modelling was used to estimate the effective connectivity between regions that showed working memory effects from the fMRI. Group-level inferences for between sessions (pre- and post-stimulation) and stimulation type (tDCS and sham) effects were carried out using the parametric empirical Bayes approach. A correlation analysis was performed to relate the estimated effective connectivity parameters of left dlPFC pre-tDCS and post-tDCS to the concentration of GABA measured via magnetic resonance spectroscopy (MRS-GABA). Next, correlation analysis was repeated using all working memory performance and all pre-tDCS and post-tDCS connectivity parameters. Results: It was found that tDCS decreased average excitatory connectivity from dlPFC to left superior frontal gyrus and increased average excitatory connectivity to left globus pallidus. Further, reduced average intrinsic (inhibitory) connectivity of left dlPFC was associated with lower MRS-GABA. However, none of the connectivity parameters of dlPFC showed any association with performance on a working memory task. Conclusions: These findings suggest that tDCS reorganised connectivity from frontal to fronto-striatal connectivity. As tDCS-related changes were not specific to the effect of working memory, they may have impacted general cognitive control processes. In addition, by reducing MRS-GABA, tDCS might make dlPFC more sen

关键词： Effective connectivity GABA Magnetic resonance spectroscopy Neurofibromatosis Non-invasive brain stimulation tDCS Working memory

来源：评论

学校读者我要写书评

暂无评论

A Novel Framework Predicting Anxiety in Chronic Disease Using Boosting Algorithm and Feature Selection Techniques 1

引用

Future of Information and Communication Conference, FICC 2024

作者： Qarmiche, N. Otmani, N. Tachfouti, N. Amara, B. Akasbi, N. Berrady, R. El Fakir, S. Laboratory of Artificial Intelligence Data Science and Emerging Systems National School of Applied Sciences Fez Sidi Mohamed Ben Abdellah University Fez Morocco Biostatistics and Informatics Unit Department of Epidemiology Clinical Research and Community Health Faculty of Medicine and Pharmacy of Fez Sidi Mohamed Ben Abdellah University Fez Morocco Department of Epidemiology Clinical Research and Community Health Faculty of Medicine and Pharmacy of Fez Sidi Mohamed Ben Abdellah University Fez Morocco Pneumology Department Hassan II University Hospital Fez Morocco Rheumatology Department Hassan II University Hospital Fez Morocco Department of Gastro Hepato-Enterology - CHU Hassan II Fez Sidi Mohammed Ben Abdellah University Fez Morocco

ISBN: (数字)9783031540530

ISBN: (纸本)9783031540523

Introduction: Chronic diseases represent a major public health challenge and contribute to a substantial number of deaths. Individuals living with chronic illnesses frequently experience anxiety due to the long-term impact on their overall well-being. The development of a predictive model to accurately identify anxiety in these patients is of great importance for healthcare providers. This study aimed to create a predictive model focused on anxiety among patients with chronic diseases within the Moroccan population. Methods and materials: In this study, a comparative analysis was performed using five different machine learning algorithms. The researchers utilized a cross-sectional dataset comprising 938 patients with chronic diseases. These patients were under monitoring at the Hassan II University Hospital Center in Fez, Morocco, from October 2019 to December 2020. Anxiety levels were evaluated using the validated Moroccan version of the Hospital Anxiety and Depression Scale (HADS). Results: To assess the models’ performance, several metrics were calculated, including accuracy, AUC, precision, recall, and F1-measure. The CatBoost algorithm achieved the highest accuracy of 0.7 in the evaluation. It also obtained an AUC of 0.68, a precision of 0.7, a recall of 0.55, and an F1-measure of 0.6. Based on its accuracy performance, the CatBoost algorithm is considered the optimal model in this study. Conclusion: This study created a CatBoost model to predict anxiety in patients with chronic diseases, offering valuable insights for anxiety prevention. Early psychological support and intervention are beneficial for high-risk patients, aiding in anxiety management. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Proxel-Based Simulation of Fault Trees in R 22

Proxel-Based Simulation of Fault Trees in R

引用

Proceedings of the Winter Simulation Conference

作者： Parisa Niloofar Hossein Haghbin Sanja Lazarova-Molnar Mærsk Mc-Kinney Møller Institute University of Southern Denmark Odense DENMARK Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr IRAN Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe GERMANY

Simulation is an advantageous solution to calculate reliability of complex systems when analytical methods are not able to deliver results in time or when we do not have the resources to apply analytical methods. Proxel-based simulation has shown to be very efficient in reliability analysis of complex systems. In this paper, we present a package called ftaproxim for proxel-based simulation of complex systems using fault trees, developed in the R programming language. ftaproxim can calculate and plot instantaneous unavailabilities of repairable events and the system as a whole.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation

arXiv

引用

arXiv 2023年

作者： Azad, Reza Niggemeier, Leon Hüttemann, Michael Kazerouni, Amirhossein Aghdam, Ehsan Khodapanah Velichko, Yury Bagci, Ulas Merhof, Dorit Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany School of Electrical Engineering Iran University of Science and Technology Tehran Iran Department of Electrical Engineering Shahid Beheshti University Tehran Iran Department of Radiology Northwestern University Chicago United States Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Medical image segmentation has seen significant improvements with transformer models, which excel in grasping far-reaching contexts and global contextual information. However, the increasing computational demands of these models, proportional to the squared token count, limit their depth and resolution capabilities. Most current methods process D volumetric image data slice-by-slice (called pseudo 3D), missing crucial inter-slice information and thus reducing the model’s overall performance. To address these challenges, we introduce the concept of Deformable Large Kernel Attention (D-LKA Attention), a streamlined attention mechanism employing large convolution kernels to fully appreciate volumetric context. This mechanism operates within a receptive field akin to self-attention while sidestepping the computational overhead. Additionally, our proposed attention mechanism benefits from deformable convolutions to flexibly warp the sampling grid, enabling the model to adapt appropriately to diverse data patterns. We designed both 2D and 3D adaptations of the D-LKA Attention, with the latter excelling in cross-depth data understanding. Together, these components shape our novel hierarchical Vision Transformer architecture, the D-LKA Net. Evaluations of our model against leading methods on popular medical segmentation datasets (Synapse, NIH Pancreas, and Skin lesion) demonstrate its superior performance. Our code implementation is publicly available at the GitHub. © 2023, CC BY.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Integration of safety means with functions of blockchain in multi-layered architecture of IoT for safer data transmission procedures 6

Integration of safety means with functions of blockchain in ...

引用

6th International Conference on Engineering and MIS, ICEMIS 2020

作者： Savukynas, Raimundas Institute of Data Science and Digital Technologies Faculty of Mathematics and Informatics Vilnius University Akademijos str. 4 VilniusLT-08412 Lithuania

ISBN: (纸本)9781450377362

The launching and linking process of heterogeneous objects to the Internet of Things (IoT) is related to some important problems of the identification, authentication for ensuring security over the wireless connections. This research study is related to the implementation of safeguard algorithms on the first stages of object identification and authentication before the permission stage for launching into the working area of the IoT. This research aims to evaluate some safety means related to the identification and authentication stages of objects by integrating them with the functionality of blockchain. The objectives of this research are related to the development of more safety working algorithms by representing the stages of checking the identity of the IoT objects. The results demonstrated integration possibilities of implementing the blockchain functionality for establishing and managing the operational rules for pre-connection stages of objects to the IoT. The paper shows new results of developing protection means for ensuring reliable communication in the transmission of outgoing confidential data and transmission data integrity from smart devices. As a result, components of necessary functional capabilities of the communication of IoT are developed by intending to ensure the safety and reliability of the wireless connection of smart objects. © 2020 ACM.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

OPTIC: Optimizing Patient-Provider Triaging & Improving Communications in Clinical Operations using GPT-4 data Labeling and Model Distillation.

arXiv

引用

arXiv 2025年

作者： Santamaria-Pang, Alberto Tuan, Frank Campbell, Ross Zhang, Cindy Jindal, Ankush Surapur, Roopa Holloman, Brad Hanisch, Deanna Buckley, Rae Cooney, Carisa Tarapov, Ivan Peairs, Kimberly S. Hasselfeld, Brian Greene, Peter Microsoft Health AI RedmondWA United States Adjunct Faculty Department of Biomedical Informatics & Data Science Johns Hopkins University BaltimoreMD United States Johns Hopkins Medicine BaltimoreMD United States University of Washington SeattleWA United States Department of Biomedical Informatics & Data Science Johns Hopkins University BaltimoreMD United States Microsoft BaltimoreMD United States Department of Plastic and Reconstructive Surgery Johns Hopkins Medicine BaltimoreMD United States Department of Plastic and Reconstructive Surgery Johns Hopkins University BaltimoreMD United States Department of Medicine Johns Hopkins University BaltimoreMD United States

Background: The COVID-19 pandemic has led to significant increases in the use of telemedicine as well as patient messaging via electronic medical portals (patient medical advice requests, or PMARs). While these systems offer benefits such as increased access to healthcare for patients, they have also created a substantial burden for healthcare providers due to surges in PMARs. This study addresses the need for an efficient tool for message triaging to reduce physician burden and improve patient-provider communication. Methods: We developed OPTIC (Optimizing Patient-Provider Triaging & Improving Communications in Clinical Operations), a robust tool for message triaging using GPT-4 for data labeling and BERT for model distillation. The study utilized a dataset of 405,487 patient messaging encounters at Johns Hopkins Medicine between January to June 2020. We employed prompt engineering strategies with GPT-4 to create a high-quality labelled dataset, which was then used to train a BERT model for classifying messages as "Admin" or "Clinical". Results: The BERT model achieved an accuracy of 88.85% on the test set derived from validated GPT-4 labeling, with a 88.29% sensitivity, 89.38% specificity, and an F1 score of 0.8842. BERTopic analysis identified 81 distinct topics within the test data, with the model achieving over 80% accuracy in classification for 58 topics. The system was successfully deployed through Epic's Nebula Cloud Platform, demonstrating its applicability in healthcare settings. Conclusion: OPTIC offers a scalable and efficient tool for triaging PMARs in health systems. Leveraging advanced natural language processing techniques can reduce the administrative burden on healthcare providers and streamline patient care coordination. © 2025, CC BY.

关键词： Labeled data

来源：评论

学校读者我要写书评

暂无评论

Implementation Analysis of KEBI Application on Non-Standard Words Detection in Student Essay Tasks on Language Variety

Implementation Analysis of KEBI Application on Non-Standard ...

引用

Information Technology International Seminar (ITIS)

作者： Ilmatus Sa' diyah Tresna Maulana Fahrudin Latinah Ibnu Zahy Atha Illah Cagiva Chaedar Bey Lima Burhan Syarif Acarya Department of Data Science Faculty of Computer Science Universitas Pembangunan Nasional &#x201C Veteran&#x201D Jawa Timur Surabaya Indonesia Department of Informatics Engineering Faculty of Computer Science Universitas Narotama Surabaya Indonesia

Non-standard word errors in writing sometimes escape the attention of writers or editors due to several factors, including unintentional or limited time in editing. Non-standard word is one of the Indonesian spelling error. Therefore, the application for detecting non-standard words in writing, KEBI, was created to minimize the height of non-standard words in writing. In this study, KEBI was analyzed in terms of application implementation to five various language essay documents as student assignments for Indonesian language courses. There are three things that are analyzed, namely the interface page, processing time, and the level of accuracy of detection and correction using the precision and recall method (confusion matrix). The overall research method includes literature study, data collection, testing, measurement of display, time, and accuracy. Based on the results of the analysis, the interface page display shows good quality. The processing time required by KEBI to detect non-standard words is in the range of 0.02-0.06 seconds. Meanwhile, KEBI's non-standard word detection accuracy rate is 86%, while KEBI's non-standard word correction accuracy rate is 90%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Unveiling the Conceptual Analysis in the Direction of Developing a Fusion Virtual Reality Model for Hearing-Impaired Visual Interaction

Unveiling the Conceptual Analysis in the Direction of Develo...

引用

International Conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： Ermiera Shafika Mokhtar Nurulnadwan Aziz Siti Salbiah Hamzah Siti Zulaiha Ahmad Ahmad Affandi Supli Azizah Che Omar College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Terengganu Dungun Terengganu Malaysia Faculty of Business and Management Universiti Teknologi MARA Cawangan Terengganu Dungun Terengganu Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Perlis Arau Perlis Malaysia School of Computing and Data Science Xiamen University Malaysia Sepang Selangor Malysia School of Multimedia Technology and Communication Universiti Utara Malaysia Sintok Kedah Malysia

ISBN: (数字)9798350348798

ISBN: (纸本)9798350348804

Virtual Reality (VR) stands out as a rapidly evolving wearable technology. However, findings from previous studies reveal that existing VR models exhibit a significant deficiency in addressing the visual interaction requirements of the hearing-impaired (HI) population. While some studies focus on developing VR for HI individuals and incorporating assistive technology (AT) concepts, however, the integration of AT, empathic design (ED), and visual interaction (VI) principles remain underexplored. This study addresses the gap by proposing a fusion VR model that prioritizes the needs of HI individuals. The challenges faced by the HI, such as difficulties in visually utilizing sign language, gestures, body language, and lip sync, underscore the importance of adopting AT, ED, and VI design principles. Through a rapid review approach, this paper strengthens the research foundation and outlines a conceptual framework for the development of a fusion VR model. This study aligns with the 12th Malaysian Plan's Pillar 5, emphasizing the empowerment of specific target groups, and supports SDG 10 by promoting social equality for the HI. The proposed fusion VR model aims to enhance existing designs by integrating AT, ED, and VI principles, providing a valuable reference for researchers in the field.

关键词： Solid modeling Visualization Analytical models Systematics Sociology Virtual reality Auditory system

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：