29 citations · 40 across the 4 of their papers we have counts for
6 papers
Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models
Jwala Dhamala, John L. Sapp, B. Milan Horácek +1
Estimation of patient-specific model parameters is important for personalized modeling, although sparse and noisy clinical data can introduce significant uncertainty in the estimat…
High-dimensional Bayesian Optimization of Personalized Cardiac Model Parameters via an Embedded Generative Model
Jwala Dhamala, Sandesh Ghimire, John L. Sapp +2
The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. However, these tissue properties are spatia…
Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization
Jwala Dhamala, Sandesh Ghimire, John L. Sapp +2
Personalization of cardiac models involves the optimization of organ tissue properties that vary spatially over the non-Euclidean geometry model of the heart. To represent the high…
Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential
Sandesh Ghimire, Jwala Dhamala, Prashnna Kumar Gyawali +3
Noninvasive reconstruction of cardiac transmembrane potential (TMP) from surface electrocardiograms (ECG) involves an ill-posed inverse problem. Model-constrained regularization is…
Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences
Sandesh Ghimire, Prashnna Kumar Gyawali, Jwala Dhamala +3
Deep learning networks have shown state-of-the-art performance in many image reconstruction problems. However, it is not well understood what properties of representation and learn…
Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection
Jwala Dhamala, Emmanuel Azuh, Abdullah Al-Dujaili +2
Timely prediction of clinically critical events in Intensive Care Unit (ICU) is important for improving care and survival rate. Most of the existing approaches are based on the app…