Calcification has significant influence over cardiovascular diseases and
interventions. Detailed characterization of calcification is thus desired for
predictive modeling, but calcified heart meshes for physics-driven simulations
are still often reconstructed using manual operations. This poses a major
bottleneck for large-scale adoption of computational simulations for research
or clinical use. To address this, we propose an end-to-end automated meshing
algorithm that enables robust incorporation of patient-specific calcification
onto a given heart mesh. The algorithm provides a substantial speed-up from
several hours of manual meshing to $\sim$1 minute of automated computation, and
it solves an important problem that cannot be addressed with recent template
registration-based heart meshing techniques. We validated our final calcified
heart meshes with extensive simulations, demonstrating our ability to
accurately model patient-specific aortic stenosis and Transcatheter Aortic
Valve Replacement. Our method may serve as an important tool for accelerating
the development and usage of physics-driven simulations for cardiac digital
twins.

我们提出了一种全自动建模算法，能够将病人特异性的钙化数据精确地添加到已有的心脏模型中，从而加速心脏数字孪生的物理模拟研究和应用的发展。

生物力学心脏数字双胞胎的稳健自动钙化网格化

Robust automated calcification meshing for biomechanical cardiac digital  twins

Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most
existing methods use feature selection methods to find the optimal subset of
voice features for all PD patients. Few have considered the heterogeneity
between patients, which implies the need to provide specific prediction models
for different patients. However, building the specific model faces the
challenge of small sample size, which makes it lack generalization ability.
Instance transfer is an effective way to solve this problem. Therefore, this
paper proposes a patient-specific game-based transfer (PSGT) method for PD
severity prediction. First, a selection mechanism is used to select PD patients
with similar disease trends to the target patient from the source domain, which
greatly reduces the risk of negative transfer. Then, the contribution of the
transferred subjects and their instances to the disease estimation of the
target subject is fairly evaluated by the Shapley value, which improves the
interpretability of the method. Next, the proportion of valid instances in the
transferred subjects is determined, and the instances with higher contribution
are transferred to further reduce the difference between the transferred
instance subset and the target subject. Finally, the selected subset of
instances is added to the training set of the target subject, and the extended
data is fed into the random forest to improve the performance of the method.
Parkinson's telemonitoring dataset is used to evaluate the feasibility and
effectiveness. Experiment results show that the PSGT has better performance in
both prediction error and stability over compared methods.

本文提出了一种基于游戏的患者特定的迁移方法（PSGT）来预测帕金森病的严重程度。该方法考虑到了患者之间的异质性，并采用实例迁移的方式来弥补患者样本数据较少的问题。通过实验验证，发现 PSGT 方法相较于其他方法具有更好的预测效果和稳定性。

帕金森病严重性预测的患者特定游戏转移方法

Patient-Specific Game-Based Transfer Method for Parkinson's Disease Severity Prediction

In today's world, many patients with cognitive impairments and motor
dysfunction seek the attention of experts to perform specific conventional
therapies to improve their situation. However, due to a lack of
neurorehabilitation professionals, patients suffer from severe effects that
worsen their condition. In this paper, we present a technological approach for
a novel robotic neurorehabilitation training system. It relies on a combination
of a rehabilitation device, signal classification methods, supervised machine
learning models for training adaptation, training exercises, and socially
interactive agents as a user interface. Together with a professional, the
system can be trained towards the patient's specific needs. Furthermore, after
a training phase, patients are enabled to train independently at home without
the assistance of a physical therapist with a socially interactive agent in the
role of a coaching assistant.

本文介绍了一种新型机器人神经康复训练系统，结合了康复设备、信号分类方法、监督式机器学习模型以及社交互动代理人作为用户界面，能够根据患者特定需求进行训练并让患者进行独立康复训练。

采用社交互动智能体进行机器人神经康复训练

Employing Socially Interactive Agents for Robotic Neurorehabilitation Training

An accurate model of patient-specific kidney graft survival distributions can
help to improve shared-decision making in the treatment and care of patients.
In this paper, we propose a deep learning method that directly models the
survival function instead of estimating the hazard function to predict survival
times for graft patients based on the principle of multi-task learning. By
learning to jointly predict the time of the event, and its rank in the cox
partial log likelihood framework, our deep learning approach outperforms, in
terms of survival time prediction quality and concordance index, other common
methods for survival analysis, including the Cox Proportional Hazards model and
a network trained on the cox partial log-likelihood.

本论文提出了一种基于深度学习的多任务学习的方法，可以直接建立患者特定的肾移植的存活函数，从而提高患者的治疗和护理共同决策的准确性和质量。