Creating a Digital Twin (DT) for Healthcare Intelligent Transportation Systems (HITS) is a hot research trend focusing on enhancing HITS management, particularly in emergencies where ambulance vehicles must arrive at the crash scene on time and track their real-time location is crucial to the medical authorities. Despite the claim of real-time representation, a temporal misalignment persists between the physical and virtual domains, leading to discrepancies in the ambulance's location representation. This study proposes integrating AI predictive models, specifically Support Vector Regression (SVR) and Deep Neural Networks (DNN), within a constructed mock DT data pipeline framework to anticipate the medical vehicle's next location in the virtual world. These models align virtual representations with their physical counterparts, i.e., metaphorically offsetting the synchronization delay between the two worlds. Trained meticulously on a historical geospatial dataset, SVR and DNN exhibit exceptional prediction accuracy in MATLAB and Python environments. Through various testing scenarios, we visually demonstrate the efficacy of our methodology, showcasing SVR and DNN's key role in significantly reducing the witnessed gap within the HITS's DT. This transformative approach enhances real-time synchronization in emergency HITS by approximately 88% to 93%.
A Revolution of Personalized Healthcare: Enabling Human Digital Twin with Mobile AIGC
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Nanyang Technological University（南洋理工大学）；Guangdong University of Technology（广东工业大学）；Concordia University（康考迪亚大学）；University of Waterloo（滑铁卢大学）
Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；School of Automation, Guangdong University of Technology（广东工业大学自动化学院）
Agent-Based Modeling and Deep Neural Networks for Establishing Digital Twins of Secure Facilities under Sensing Restrictions
Oak Ridge National Laboratory（橡树岭国家实验室）；University of Central Florida（中佛罗里达大学）；NBC Universal Studios（NBC环球影城）；SimCentric Technologies（SimCentric科技公司）；University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）；Oak Ridge Institute for Science and Education（橡树岭科学与教育研究所）
Personalized Heart Disease Detection via ECG Digital Twin Generation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Guangdong Provincial People’s Hospital, Southern Medical University（南方医科大学广东省人民医院）；Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences（广东省人民医院广东省心血管病研究所、广东省医学科学院）；School of Public Health, Zhejiang University（浙江大学公共卫生学院）
TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model
School of Public Health, Zhejiang University（浙江大学公共卫生学院）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Analyzing the Variations in Emergency Department Boarding and Testing the Transferability of Forecasting Models across COVID-19 Pandemic Waves in Hong Kong: Hybrid CNN-LSTM approach to quantifying building-level socioecological risk
JC School of Public Health and Primary Care, The Chinese University of Hong Kong（香港中文大学赛马会公共卫生及基层医疗学院）；Hong Kong Polytechnic University（香港理工大学）
A Temporal Machine Learning-Based Time-to-Event Model for Predicting ALS Progression and Healthcare Utilization
Harrison College of Pharmacy, Auburn University（奥本大学哈里森药学院）；Fisk University（菲斯克大学）；Lewis Katz School of Medicine, Temple University（天普大学刘易斯·卡茨医学院）；Emory University（埃默里大学）；Barnett College of Public Health, Temple University（天普大学巴尼特公共卫生学院）
