@article{15972, keywords = {High-dimensional Bayesian optimization, variational autoencoder, personalized modeling}, author = {Jwala Dhamala and Pradeep Bajracharya and Hermenegild Arevalo and John Sapp and Milan Hor{\'a}cek and Katherine Wu and Natalia Trayanova and Linwei Wang}, title = {Embedding high-dimensional Bayesian optimization via generative modeling: Parameter personalization of cardiac electrophysiological models}, abstract = {The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. Because tissue properties are spatially varying across the underlying geometrical model, it presents a significant challenge of high-dimensional (HD) optimization at the presence of limited measurement data. A common solution to reduce the dimension of the parameter space is to explicitly partition the geometrical mesh. In this paper, we present a novel concept that uses a generative variational auto-encoder (VAE) to embed HD Bayesian optimization into a low-dimensional (LD) latent space that represents the generative code of HD parameters. We further utilize VAE-encoded knowledge about the generative code to guide the exploration of the search space. The presented method is applied to estimating tissue excitability in a cardiac electrophysiological model in a range of synthetic and real-data experiments, through which we demonstrate its improved accuracy and substantially reduced computational cost in comparison to existing methods that rely on geometry-based reduction of the HD parameter space.}, year = {2020}, journal = {Medical Image Analysis}, volume = {62}, pages = {101670}, publisher = {Elsevier}, issn = {1361-8415}, url = {http://www.sciencedirect.com/science/article/pii/S1361841520300360}, doi = {https://doi.org/10.1016/j.media.2020.101670}, }