Digital twin technology has is anticipated to transform healthcare, enabling personalized medicines and support, earlier diagnoses, simulated treatment outcomes, and optimized surgical plans. Digital twins are readily gaining traction in industries like manufacturing, supply chain logistics, and civil infrastructure. Not in patient care, however. The challenge of modeling complex diseases with multimodal patient data and the computational complexities of analyzing it have stifled digital twin adoption in the biomedical vertical. Yet, these major obstacles can potentially be handled by approaching these models in a different way. This paper proposes a novel framework for addressing the barriers to clinical twin modeling created by computational costs and modeling complexities. We propose structuring patient health data as a knowledge graph and using closed-form continuous-time liquid neural networks, for real-time analytics. By synthesizing multimodal patient data and leveraging the flexibility and efficiency of closed form continuous time networks and knowledge graph ontologies, our approach enables real time insights, personalized medicine, early diagnosis and intervention, and optimal surgical planning. This novel approach provides a comprehensive and adaptable view of patient health along with real-time analytics, paving the way for digital twin simulations and other anticipated benefits in healthcare.
Virtual Patients, Real Gains: Digital Twin-Based Simulated CT for Multitask Lung Nodule Analysis
Center for Virtual Imaging trials, Dept. of Radiology, Duke University Medical Center（虚拟成像中心，放射科，杜克大学医学中心）；Dept. of Electrical and Computer Engineering, Pratt School of Engineering, Duke University（电气与计算机工程系，工程学院，杜克大学）
Ensuring Safety in Automated Mechanical Ventilation through Offline Reinforcement Learning and Digital Twin Verification
School of Engineering, University of Warwick, Coventry, UK（沃里克大学工程学院，英国考文垂）；School of Computer Science, Chongqing University, China（重庆大学计算机科学学院，中国）；School of Computer Science and Technology, Hainan University, China（海南大学计算机科学与技术学院，中国）；Institute for Computational Biomedicine, RWTH Aachen University, Germany（鲁姆哈根工业大学计算生物医学研究所，德国）
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）
EHR-MPC: Inference-Time Control for Sepsis Treatment with Generative Patient Digital Twins
Broad Institute of MIT and Harvard（MIT和哈佛大学Broad研究所）；Harvard University（哈佛大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Technion–Israel Institute of Technology（技术学院-以色列理工学院）
GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals
College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；Department of Endocrinology, Mayo Clinic Arizona（梅奥诊所亚利桑那分部内分泌科）
Cardiac Digital Twins at Scale from MRI: Open Tools and Representative Models from ~55000 UK Biobank Participants
School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学学院）；The Cardiac Electro-Mechanics Research Group, Imperial College London（伦敦帝国理工学院心脏电机械学研究组）；The Alan Turing Institute, London（伦敦艾伦·图灵研究所）；Department of Cardiology, University Medical Center Utrecht（乌得勒支大学医学中心心内科部）；ETH Zurich（苏黎世联邦理工学院）
A Hybrid Recommender System for Patient-Doctor Matchmaking in Primary Care
Universidade Nova de Lisboa, School of Business and Economics（新里斯本大学商学院与经济学院）；University of California, Davis（加利福尼亚大学戴维斯分校）；Wright State University（莱特州立大学）
A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients
Massachusetts Institute of Technology（麻省理工学院）；University of Massachusetts（马萨诸塞大学）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Massachusetts General Hospital（麻省总医院）；Tufts University School of Medicine（塔夫茨大学医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Washington University School of Medicine（华盛顿大学医学院）；Philips Research North America（飞利浦北美研究院）；Harvard University（哈佛大学）；Adobe Research（Adobe研究院）
Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation
New Mexico Institute of Mining and Technology（新墨西哥矿业技术学院）；Oak Ridge National Laboratory（橡树岭国家实验室）；The University of Tennessee Health Science Center（田纳西大学健康科学中心）；Illinois State University（伊利诺伊州立大学）
PRECISE Framework: GPT-based Text For Improved Readability, Reliability, and Understandability of Radiology Reports For Patient-Centered Care
Massachusetts General Hospital（麻省总医院）；Brigham and Women's Hospital（布列根和妇女医院）；Athinoula A. Martinos Center for Biomedical Imaging（阿西诺拉·A·马蒂诺斯生物医学成像中心）；Harvard Medical School（哈佛医学院）；Julia R. Masterman High School（朱莉娅·R·马斯特曼高中）
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）
From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care
Utrecht University（乌特勒支大学）；Delft University of Technology（代尔夫特理工大学）；University Medical Center Utrecht（乌特勒支大学医学中心）；Organisation for Economic Co-operation and Development（经济合作与发展组织）
Computable Phenotypes of Patient Acuity in the Intensive Care Unit
Intelligent Clinical Care Center (IC3), University of Florida（佛罗里达大学智能临床护理中心（IC3））；College of Medicine, University of Florida（佛罗里达大学医学院）；J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida（佛罗里达大学J.克雷顿·普鲁伊特家族生物医学工程系）
