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或治疗的反应 长时间的刺激和适应在精确医学中仍然是一个巨大的挑战。 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。此外，它还 防止基于流的动态数据实时监控患者的血流动力学 比如从可穿戴设备获得的产品。通过从只能捕获几个 从心跳到几个月甚至几年的建模，我们将使用特定于患者的数字 双胞胎，提供对患者血流动力学状态的按需跟踪。这样的数据将 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果来制定治疗计划。这个 这项提议的主要目标是开发和应用一种结合基于物理的 机器学习模拟，与可穿戴传感器相结合，提供实时、 对数月至数年的3D复杂血流动力学模式进行个性化预测。更好的 了解患者的循环系统和潜在的血流动力学反应 在不同的生理状态下随时间推移具有广泛的相关性 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.