High-dimensional Bayesian Optimization of Personalized Cardiac Model Parameters via an Embedded Generative Model

Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds

Models of electrical excitation and recovery in the heart have become increasingly detailed, but have yet to be used routinely in the clinical setting to guide personalized intervention in patients. One of the main challenges is calibrating models from the limited measurements that can be made in a patient during a standard clinical procedure. In this work, we propose a novel framework for the probabilistic calibration of electrophysiology parameters on the left atrium of the heart using local measurements of cardiac excitability. Parameter fields are represented as Gaussian processes on manif…

从运动到意义：基于生物力学信息的神经网络用于可解释的心血管疾病识别

From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification

本研究提出一种结合深度学习图像配准与物理信息正则化的生物力学信息神经网络，在ACDC数据集上实现高精度心脏疾病分类，并通过可解释AI提供透明预测。

Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models

Estimation of patient-specific model parameters is important for personalized modeling, although sparse and noisy clinical data can introduce significant uncertainty in the estimated parameter values. This importance source of uncertainty, if left unquantified, will lead to unknown variability in model outputs that hinder their reliable adoptions. Probabilistic estimation model parameters, however, remains an unresolved challenge because standard Markov Chain Monte Carlo sampling requires repeated model simulations that are computationally infeasible. A common solution is to replace the simula…

个性化且不确定性感知的冠状动脉血流动力学模拟：从贝叶斯估计到改进的多保真度不确定性量化

Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification

本文提出一种端到端不确定性感知的冠状动脉血流模拟框架，通过贝叶斯估计个性化分支血流，并结合多保真度蒙特卡洛与降维方法，以更低成本实现更高精度的临床与生物力学量预测。

使用采样机制的个性化 DP-SGD

Personalized DP-SGD using Sampling Mechanisms

本文扩展DP-SGD以支持($Φ$,$Δ$)-个性化差分隐私，通过在DP-SGD迭代中嵌入多轮个性化采样机制，满足用户不同隐私级别需求，显著提升了模型性能与效率。

Personalized Dynamic Treatment Regimes in Continuous Time: A Bayesian Approach for Optimizing Clinical Decisions with Timing

Accurate models of clinical actions and their impacts on disease progression are critical for estimating personalized optimal dynamic treatment regimes (DTRs) in medical/health research, especially in managing chronic conditions. Traditional statistical methods for DTRs usually focus on estimating the optimal treatment or dosage at each given medical intervention, but overlook the important question of "when this intervention should happen." We fill this gap by developing a two-step Bayesian approach to optimize clinical decisions with timing. In the first step, we build a generative model for…