论文概要
研究领域: ML 作者: Sokratis J. Anagnostopoulos, George Rovas, Vasiliki Bikia 等 发布时间: 2026-04-03 arXiv: 2604.03197
中文摘要
心血管建模在过去几十年中迅速发展，原因是健康跟踪和心血管疾病早期检测的需求日益增加。虽然一维动脉模型在计算效率和解决方案保真度之间提供了有吸引力的折衷，但将其应用于大规模人群或生成大型虚拟队列仍然具有挑战性。本文提出一个系统框架，用于训练能够瞬时预测血流动力学和估计参数的机器学习模型。
原文摘要
Cardiovascular modeling has rapidly advanced over the past few decades due to the rising needs for health tracking and early detection of cardiovascular diseases. While 1-D arterial models offer an attractive compromise between computational efficiency and solution fidelity, their application on large populations or for generating large \emph{in silico} cohorts remains challenging. Certain hemodynamic parameters like the terminal resistance/compliance, are difficult to clinically estimate and often yield non-physiological hemodynamics when sampled naively, resulting in large portions of simulated datasets to be discarded. In this work, we present a systematic framework for training machine learning (ML) models, capable of instantaneous hemodynamic prediction and parameter estimation. We in...
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