Physics-constrained deep active learning for spatiotemporal modeling of cardiac electrodynamics
The development of computational modeling and simulation have immensely benefited the
study of cardiac disease mechanisms and facilitated the optimal disease diagnosis and …
study of cardiac disease mechanisms and facilitated the optimal disease diagnosis and …
Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is
critical to timely medical treatment to save patients' lives. Routine use of the …
critical to timely medical treatment to save patients' lives. Routine use of the …
Multi-branching temporal convolutional network for sepsis prediction
Sepsisis among the leading causes of morbidity and mortality in modern intensive care
units. Accurate sepsis prediction is of critical importance to save lives and reduce medical …
units. Accurate sepsis prediction is of critical importance to save lives and reduce medical …
Physics-constrained deep learning for robust inverse ecg modeling
The rapid development in advanced sensing and imaging brings about a data-rich
environment, facilitating the effective modeling, monitoring, and control of complex systems …
environment, facilitating the effective modeling, monitoring, and control of complex systems …
Hierarchical active learning for defect localization in 3d systems
Aim: Advanced sensing and imaging is capable to retrieve rich information of complex
systems, which can be integrated with underlying physics to develop a personalized …
systems, which can be integrated with underlying physics to develop a personalized …
Multi-source data and knowledge fusion via deep learning for dynamical systems: applications to spatiotemporal cardiac modeling
B Yao - IISE Transactions on Healthcare Systems Engineering, 2024 - Taylor & Francis
Advanced sensing and imaging provide unprecedented opportunities to collect data from
diverse sources for increasing information visibility in spatiotemporal dynamical systems …
diverse sources for increasing information visibility in spatiotemporal dynamical systems …
Multi-branching Temporal Convolutional Network with Tensor Data Completion for Diabetic Retinopathy Prediction
Diabetic retinopathy (DR), a microvascular complication of diabetes, is the leading cause of
vision loss among working-aged adults. However, due to the low compliance rate of DR …
vision loss among working-aged adults. However, due to the low compliance rate of DR …
Simulation Optimization of Spatiotemporal Dynamics in 3D Geometries
Many engineering and healthcare systems are featured with spatiotemporal dynamic
processes. The optimal control of such systems often involves sequential decision making …
processes. The optimal control of such systems often involves sequential decision making …
An Electrocardiogram Classification Using a Multiscale Convolutional Causal Attention Network
C Guo, B Yin, J Hu - Electronics, 2024 - mdpi.com
Electrocardiograms (ECGs) play a pivotal role in the diagnosis and prediction of
cardiovascular diseases (CVDs). However, traditional methods for ECG classification …
cardiovascular diseases (CVDs). However, traditional methods for ECG classification …
MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Records
The era of big data has made vast amounts of clinical data readily available, particularly in
the form of electronic health records (EHRs), which provides unprecedented opportunities …
the form of electronic health records (EHRs), which provides unprecedented opportunities …