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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.

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Toward the Implementation of Hiroshima University Local 5G Communication Infrastructure in a Digital Twin Environment

Toward the Implementation of Hiroshima University Local 5G C...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Tomohiro Taniguchi Takumi Harada Hiromu Shimoji Kouji Nishimura Graduate School of Advanced Science and Engineering Hiroshima University Higashihiroshima Hiroshima Japan School of Informatics and Data Science Hiroshima University Higashihiroshima Hiroshima Japan Information Media Center Hiroshima University Higashihiroshima Hiroshima Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Local 5G refers to a private 5G network that can be independently constructed and operated by various entities, such as municipalities and enterprises. It is highly anticipated as a technology capable of addressing diverse societal challenges. In August 2023, a local 5G system was deployed at Hiroshima University's Higashi-Hiroshima campus, intended for use as a testbed environment for practical applications and experimental verification. In this research, we have measured radio wave propagation characteristics and transmission performance to comprehensively understand the unique features and fundamental capabilities of the local 5G system within the campus environment. Based on these measurement results, a visualization application is being developed to represent the findings effectively. However, experimental and measurement activities in physical environments often face constraints such as time, space, and physical limitations. To overcome these challenges and provide users with more flexible opportunities to validate experiments on the local 5G, this study aims to build a digital twin model of the local 5G testbed environment and implement it within the visualization application. This approach seeks to clarify the system's characteristics and performance further. This paper introduces the radio and communication environment visualization application and discusses the conceptual design and prospects for integrating the digital twin model into the system.

关键词： Visualization 5G mobile communication Propagation Extraterrestrial measurements Time measurement Digital twins Time factors Faces

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Effects of Tirzepatide in Type 2 Diabetes: Individual Variation and Relationship to Cardiometabolic Outcomes

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Journal of the American College of Cardiology 2025年第19期85卷 1858-1872页

作者： Aminorroaya, Arya Oikonomou, Evangelos K. Biswas, Dhruva Jastreboff, Ania M. Khera, Rohan Section of Cardiovascular Medicine Department of Internal Medicine Yale School of Medicine New Haven CT United States Cardiovascular Data Science (CarDS) Lab Yale School of Medicine New Haven CT United States Section of Endocrinology and Metabolism Department of Medicine Yale School of Medicine New Haven CT United States Section of Pediatric Endocrinology Department of Pediatrics Yale School of Medicine New Haven CT United States Yale Obesity Research Center (Y-Weight) Yale School of Medicine New Haven CT United States Center for Outcomes Research and Evaluation (CORE) Yale New Haven Hospital New Haven CT United States Section of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT United States Section of Health Informatics Department of Biostatistics Yale School of Public Health New Haven CT United States

Background: Tirzepatide—a dual GIP/GLP-1 receptor agonist—exerts pleiotropic effects on cardiometabolic health. Objectives: The authors sought to investigate the efficacy of tirzepatide in improving different cardiometabolic risk factors across individuals and subpopulations. Methods: Using an independent, global data-sharing and analytics platform, we performed an individual participant data meta-analysis by pooling data from 7 Phase 3 randomized clinical trials that compared tirzepatide with placebo or standard antihyperglycemic agents in individuals with type 2 diabetes. The study outcomes were the presence of a range of cardiometabolic abnormalities, representing components of metabolic syndrome (MetS) (elevated waist circumference, triglycerides, blood pressure, and fasting blood glucose, and decreased high-density lipoprotein cholesterol), as well as elevated body mass index and MetS (≥3 cardiometabolic abnormalities). Outcomes were modeled using mixed-effects models, with inverse probability weighting to account for study design differences. Results: We included 7,805 participants with a weighted median age of 59 years (Q1-Q3: 51-66 years) and 43.2% women. Over a weighted median treatment duration of 41.0 weeks, tirzepatide reduced the odds of all cardiometabolic abnormalities, ranging from 34% reduction for the odds of decreased high-density lipoprotein cholesterol (OR: 0.66 [95% CI: 0.52-0.84]) to 96% reduction in the odds of elevated body mass index (OR: 0.04 [95% CI:0.02-0.08]), and 72% reduction for the odds of MetS (OR: 0.28 [95% CI: 0.24-0.33]). Tirzepatide's superior efficacy in resolving MetS was consistent across demographic and clinical subpopulations, with higher efficacy in age <65 years vs ≥65 years, and in individuals without vs with baseline use of sodium-glucose cotransporter 2 inhibitors (P for interaction = 0.008 and 0.009, respectively). Conclusions: This pooled analysis suggests that tirzepatide may improve cardiometabolic abnormalities a

关键词： diabetes mellitus type 2 heart disease risk factors meta-analysis randomized controlled trial tirzepatide

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DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets

DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haiyang Wang Chen Shi Shaoshuai Shi Meng Lei Sen Wang Di He Bernt Schiele Liwei Wang Center for Data Science Peking University National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Max Planck Institute for Informatics Saarland Informatics Campus Zhejiang Lab Huawei

Designing an efficient yet deployment-friendly 3D backbone to handle sparse point clouds is a fundamental problem in 3D perception. Compared with the customized sparse convolution, the attention mechanism in Transformers is more appropriate for flexibly modeling long-range relationships and is easier to be deployed in real-world applications. However, due to the sparse characteristics of point clouds, it is non-trivial to apply a standard transformer on sparse points. In this paper, we present Dynamic Sparse Voxel Transformer (DSVT), a single-stride window-based voxel Transformer backbone for outdoor 3D perception. In order to efficiently process sparse points in parallel, we propose Dynamic Sparse Window Attention, which partitions a series of local regions in each window according to its sparsity and then computes the features of all regions in a fully parallel manner. To allow the cross-set connection, we design a rotated set partitioning strategy that alternates between two partitioning configurations in consecutive self-attention layers. To support effective downsampling and better encode geometric information, we also propose an attention-style 3D pooling module on sparse points, which is powerful and deployment-friendly without utilizing any customized CUDA operations. Our model achieves state-of-the-art performance with a broad range of 3D perception tasks. More importantly, DSVT can be easily deployed by TensorRT with real-time inference speed (27Hz). Code will be available at https://***/Haiyang-W/DSVT.

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A Comparative Study of Recent Large Language Models on Generating Hospital Discharge Summaries for Lung Cancer Patients

arXiv

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arXiv 2024年

作者： Li, Yiming Li, Fang Roberts, Kirk Cui, Licong Tao, Cui Xu, Hua McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Department of Artificial Intelligence and Informatics Mayo Clinic JacksonvilleFL32224 United States Department of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT06510 United States

Objective Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have significantly enhanced their capability in understanding and summarizing complex medical texts. This research aims to explore how LLMs can alleviate the burden of manual summarization, streamline workflow efficiencies, and support informed decision-making in healthcare settings. Materials and Methods Clinical notes from a cohort of 1,099 lung cancer patients were utilized, with a subset of 50 patients for testing purposes, and 102 patients used for model fine-tuning. This study evaluates the performance of multiple LLMs, including GPT-3.5, GPT-4, GPT-4o, and LLaMA 3 8b, in generating discharge summaries. Evaluation metrics included token-level analysis (BLEU, ROUGE-1, ROUGE-2, ROUGE-L) and semantic similarity scores between model-generated summaries and physician-written gold standards. LLaMA 3 8b was further tested on clinical notes of varying lengths to examine the stability of its performance. Results The study found notable variations in summarization capabilities among LLMs. GPT-4o and fine-tuned LLaMA 3 demonstrated superior token-level evaluation metrics, while LLaMA 3 consistently produced concise summaries across different input lengths. Semantic similarity scores indicated GPT-4o and LLaMA 3 as leading models in capturing clinical relevance. Conclusion This study contributes insights into the efficacy of LLMs for generating discharge summaries, highlighting LLaMA 3's robust performance in maintaining clarity and relevance across varying clinical contexts. These findings underscore the potential of automated summarization tools to enhance documentation precision and efficiency, ultimately improving patient care and operational capability in healthcare settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Lung cancer

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Internet Service Assessment in Lombok Barat Regency

Internet Service Assessment in Lombok Barat Regency

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International Conference on Information Technology Systems and Innovation (ICITSI)

作者： Sumirah Budi Nugroho Andrari Grahitandaru West Lombok Regency Office of Communication Informatics and Statistics West Lombok Indonesia Research and Innovation National Agency Res. Center for Data and Information Science Bandung Indonesia

ISBN: (数字)9798331511470

ISBN: (纸本)9798331511487

The rapid development of digital technology has significantly transformed governance, mainly through internet services. This study examines internet services governance within the Communication, informatics, and Statistics Office of West Lombok Regency, which strategically supports digital government. While the vast potential of internet services can enhance efficiency, transparency, and accountability, several challenges, such as inadequate regulations, limited human resources, and infrastructure, hinder its complete optimization. Using the Information and Communication Technology governance framework, this research explores the implementation of extensive data management and provides recommendations to strengthen existing governance practices. Additionally, the study focuses on Internet service management, which is essential for improving governance quality and public service delivery. Despite the potential benefits, internet service governance faces similar challenges, particularly regarding uneven bandwidth distribution among government offices. This imbalance results in inefficiencies, with some offices lacking sufficient resources to operate effectively, leading to administrative delays and disruptions in service delivery. The research uses descriptive statistical analysis to evaluate data from 44 government offices, assessing user numbers, bandwidth allocation, and usage efficiency. Linear regression and k-means clustering are applied to model internet service maturity and classify offices based on usage patterns. The findings reveal substantial disparities in user numbers, bandwidth distribution, and efficiency ratios, with average users and bandwidth standing at 108.02 and 166.14 mbps, respectively. Clustering analysis identified three primary groups, emphasizing the need for further assessment to improve bandwidth allocation and ensure fair distribution based on each agency’s specific requirements.

关键词： Technological innovation Statistical analysis Web and internet services Government Linear regression Bandwidth Channel allocation Regulation Information and communication technology Optimization

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E-Government Interoperability: Provincial-Level Architecture Model to Enable Fast Healthcare Interoperability Resources (FHIR) 9

E-Government Interoperability: Provincial-Level Architecture...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Wijayanti, Diana Urbaya, Satriawansyah Sitompul, Taufiq Adrian, Verry BINUS Graduate Program Bina Nusantara University Department of Information Systems Jakarta11480 Indonesia Program USAID Indonesia Jakarta12930 Indonesia University of Oslo Department of Informatics Oslo0316 Norway Binus Graduate Program - Doctor Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Jakarta Provincial Health Office Center of Data and Information Technology Jakarta10160 Indonesia

ISBN: (纸本)9798350390025

The process of using ICT to provide services to the public is known as the Indonesian e-Government system, or Sistem Pemerintahan Berbasis Elektronik (SPBE). The e-Government initiative in Jakarta Provincial Health Office involves enhancing collaboration among public health entities for efficient data exchange and streamlined processes, especially between the Provincial and District Health Offices, public hospitals, government clinics, and primary health care centers (Puskesmas). Achieving interoperability requires standardized protocols and a well-defined architectural model to integrate data seamlessly. This study presents a provincial-level architectural model focused on improving electronic health records interoperability, aiming to promote the adoption of the national Fast Healthcare Interoperability Resources (FHIR) health information exchange platform and enhance the integrity of health data in Jakarta. The study methodology involves conducting literature reviews, observations, and discussions with representatives from healthcare facilities to develop the e-Government architecture model and prototype of the infrastructure layer aiming to facilitate the interoperability of Electronic Health Records (EHRs) across 93 healthcare facilities, all of which are part of the SPBE users. © 2024 IEEE.

关键词： e-government

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A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factors

arXiv

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arXiv 2023年

作者： Li, Can Jiang, Xiaoqian Zhang, Kai Department of Biostatistics and Data Science School of Public Health The University of Texas Health Science Center at Houston HoustonTX United States Department of Health Data Science and Artificial Intelligence McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX United States

Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient for a donor and ensuring transplant equity among different subpopulations. The current MELD scoring system evaluates a patient's mortality risk if not receiving an organ within 90 days. However, the donor-patient matching should also consider posttransplant risk factors, such as cardiovascular disease, chronic rejection, etc., which are all common complications after transplant. Accurate prediction of these risk scores remains a significant challenge. In this study, we used predictive models to solve the above challenges. Specifically, we proposed a deep learning model to predict multiple risk factors after a liver transplant. By formulating it as a multi-task learning problem, the proposed deep neural network was trained to simultaneously predict the five posttransplant risks and achieve equal good performance by exploiting task-balancing techniques. We also proposed a novel fairness-achieving algorithm to ensure prediction fairness across different subpopulations. We used electronic health records of 160,360 liver transplant patients, including demographic information, clinical variables, and laboratory values, collected from the liver transplant records of the United States from 1987 to 2018. The model's performance was evaluated using various performance metrics such as AUROC and AUPRC. Our experiment results highlighted the success of our multitask model in achieving task balance while maintaining accuracy. The model significantly reduced the task discrepancy by 39%. Further application of the fairness-achieving algorithm substantially reduced fairness disparity among all sensitive attributes (gender, age group, and race/ethnicity) in each risk factor. It underlined the potency of integrating fairness considerations into the task-balancing framework, ensuring robust and fair predictions

关键词： Forecasting

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FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation

arXiv

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arXiv 2023年

作者： Li, Can Lai, Dejian Jiang, Xiaoqian Zhang, Kai Department of Biostatistics and Data Science School of Public Health The University of Texas Health Science Center at Houston HoustonTX United States Department of Health Data Science and Artificial Intelligence McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX United States

Liver transplantation often faces fairness challenges across subgroups defined by sensitive attributes such as age group, gender, and race/ethnicity. Machine learning models for outcome prediction can introduce additional biases. Therefore, we introduce Fairness through the Equitable Rate of Improvement in Multitask Learning (FERI) algorithm for fair predictions of graft failure risk in liver transplant patients. FERI constrains subgroup loss by balancing learning rates and preventing subgroup dominance in the training process. Our results show that FERI maintained high predictive accuracy with AUROC and AUPRC comparable to baseline models. More importantly, FERI demonstrated an ability to improve fairness without sacrificing accuracy. Specifically, for the gender, FERI reduced the demographic parity disparity by 71.74%, and for the age group, it decreased the equalized odds disparity by 40.46%. Therefore, the FERI algorithm advanced fairness-aware predictive modeling in healthcare and provides an invaluable tool for equitable healthcare systems. © 2023, CC BY.

关键词： Contrastive Learning

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Nice Cross-gramian, the Generalized Symmetry and Determination of Controller Structure for Multivariable Switched Systems

Nice Cross-gramian, the Generalized Symmetry and Determinati...

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2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Tahavori, Maryamsadat Shaker, Hamid Reza Dashti, Rahman Technical University of Denmark DTU Engineering Technology Ballerup Denmark Center for Energy Informatics University of Southern Denmark Odense Denmark Persian Gulf University Faculty of Intelligent Systems Engineering & Data Science Bushehr*** Iran

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

In this paper, the determination of a control struc-ture for discrete-time MIMO switched systems is addressed. In the first step, for discrete-time switched systems, the concept of nice cross-gramians is proposed and investigation is done on whether the suggested nice cross-gramian exists. It has been demonstrated that the nice cross-gramian can be achieved as a solution to a specific algebraic matrix equation, if it exists. While the proposed cross-gramian has a wide range of uses, including model reduction, in this paper, it has been utilized to form the Nice Hankel Interaction Index Array (NHIIA) for control structure selection. The control structure determination strategy which is proposed is one of the few that supports switched systems, takes advantage of gramian-based techniques, and is computationally more effective than its only counterpart. © 2022 IEEE.

关键词： Matrix algebra

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