论文标题
图表预测患者的医疗状况：迈向数字双胞胎
Graph representation forecasting of patient's medical conditions: towards a digital twin
论文作者
论文摘要
目的：现代医学需要从等待和反应，治愈性纪律转变为预防性的跨学科科学，旨在为患者提供个性化，系统和精确的治疗计划。这项工作的目的是介绍机器学习方法与机械计算建模的整合如何产生可靠的基础架构以运行概率模拟，从而在整个生物体中整体认为整个生物体。方法：我们提出了一个一般框架，该框架构成了高级AI方法并集成了数学建模，以便对当前和将来的生理条件提供全景。所提出的体系结构基于图形神经网络（GNN）预测临床相关的终点（例如血压）和生成性对抗网络（GAN），提供了转录组合性概念的证明。结果：我们显示了ACE2在多个组织中不同信号通路对心血管功能的不同信号通路的过表达的病理作用的结果。我们提供了使用分子数据来驱动局部和全球临床参数的大量组合临床模型的概念证明，并得出代表患者生理状态的进化的未来轨迹。意义：我们认为，计算患者的图表表示可以解决将多尺度计算建模与AI整合的重要技术挑战。我们认为，这项工作代表着向医疗保健数字双胞胎迈出的一步。
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.
