Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalised, systemic and precise treatment plans to patients. The aim of this work is to present how the integration of machine learning approaches with mechanistic computational modelling could yield a reliable infrastructure to run probabilistic simulations where the entire organism is considered as a whole. Methods: We propose a general framework that composes advanced AI approaches and integrates mathematical modelling in order to provide a panoramic view over current and future physiological conditions. The proposed architecture is based on a graph neural network (GNNs) forecasting clinically relevant endpoints (such as blood pressure) and a generative adversarial network (GANs) providing a proof of concept of transcriptomic integrability. Results: We show the results of the investigation of pathological effects of overexpression of ACE2 across different signalling pathways in multiple tissues on cardiovascular functions. We provide a proof of concept of integrating a large set of composable clinical models using molecular data to drive local and global clinical parameters and derive future trajectories representing the evolution of the physiological state of the patient. Significance: We argue that the graph representation of a computational patient has potential to solve important technological challenges in integrating multiscale computational modelling with AI. We believe that this work represents a step forward towards a healthcare digital twin.
Reconstructing and forecasting disease trajectories of patients with Alzheimer's disease using routine data in resource-constrained settings
Yardi School of Artificial Intelligence (ScAI), Indian Institute of Technology Delhi（印度理工学院德里分校亚迪人工智能学院）；Department of Neurology, Vardhman Mahavir Medical College and Safdarjung Hospital（瓦尔丹·马哈维尔医学院和萨夫达戎医院神经内科）；Department of Applied Mechanics, Indian Institute of Technology Delhi（印度理工学院德里分校应用力学系）
ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of North Carolina（北卡罗来纳大学）；SRI International（SRI国际）；California Institute of Technology（加州理工学院）
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）
Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors
Technical University of Munich (TUM), School of Medicine and Health, Klinikum rechts der Isar, TUM University Hospital（慕尼黑技术大学（TUM）、医学院与健康学院、右岸医院、TUM大学医院）；Center for Personalized Medicine (ZPM)（个性化医学中心）；German Cancer Consortium (DKTK)（德国癌症联合会（DKTK））；EUropean Reference Network for RAre CANcers (EURACAN) Initiative（罕见癌症欧洲参考网络（EURACAN）计划）；University of Mainz Medical Center（法兰克福大学医学中心）；Google Cloud（谷歌云）；Institute of Artificial Intelligence in Medicine and Healthcare（医学与健康人工智能研究所）
