Dynamic models of the cardiovascular system capturing years, rather than heartbeats

预测特定患者的血管系统对不同治疗的反应， 长期的刺激和适应仍然是精准医疗的巨大挑战。 缺乏实时周转严重限制了我们搜索广泛治疗空间的能力， 根据长期、个性化的预测确定最佳干预计划。而且 防止基于流式动态数据对患者的血液动力学进行实时监测 例如从可穿戴设备获取的信息。通过从只能捕捉到几个 心跳建模几个月甚至几年，我们将患者特定的数字 双胞胎，以提供对患者血液动力学状态的按需跟踪。这些数据将 改善对心血管疾病的筛查，改善监测，最后， 通过预测目前无法实现的长期流动效应来制定治疗计划。的 该提案的主要目标是开发和应用一种方法， 通过机器学习进行模拟，结合可穿戴传感器， 个性化的3D预测，复杂的血流动力学模式，持续数月至数年。更好的 了解患者的循环系统和基础血流动力学如何响应 在不同的生理状态下，随着时间的推移， 血管疾病

Predicting how a particular patient's vascular system with respond to different treatment or stimuli and adapt over long periods of time remains a grand challenge in precision medicine. The lack of real-time turn around critically limits our ability to search a wide treatment space to identify optimal intervention plans based on long-term, personalized predictions. Moreover, it prevents real-time monitoring of a patient's hemodynamics based on streaming, dynamic data such as that acquired from wearables. By moving from simulations that can capture only several heartbeats to modeling months or even years, we shift the utilization of patient-specific digital twins to provide on-demand tracking of a patient's hemodynamic state. Such data would improve screening for cardiovascular disease, improved monitoring, and finally, inform treatment planning by enabling prediction of longterm flow effects currently not attainable. The major objective of this proposal is to develop and apply a methodology coupling physics-based simulations with machine learning that, combined with wearable sensors, provides real-time, personalized predictions of 3D, complex hemodynamic patterns over months to years. A better understanding of how a patient's circulatory system and underlying hemodynamics responds under different physiological states over time is of broad relevance to treating a wide range of vascular diseases.