Bayesian Inference-Based Gaussian Mixture Models With Optimal Components Estimation Towards Large-Scale Synthetic Data Generation for In Silico Clinical Trials

• Published in: IEEE open journal of engineering in medicine and biology Vol. 3; pp. 108 - 114
• Main Authors: Pezoulas, Vasileios C., Tachos, Nikolaos S., Gkois, George, Olivotto, Iacopo, Barlocco, Fausto, Fotiadis, Dimitrios I.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> in silico</tex-math> </inline-formula> </named-content> clinical trials; <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <tex-math notation="LaTeX">$in silico$</tex-math> </named-content> clinical trials; Algorithms; Bayesian analysis; Cardiology; Cardiomyopathy; Clinical diagnosis; Clinical trials; Clustering; Coefficient of variation; computational complexity; Computational efficiency; Computational modeling; Computer applications; Correlation; Eigenvalues; Estimation; Gaussian Mixture Models; Gaussian processes; Generators; hypertrophic cardiomyopathy; Mathematical models; Mixtures; Population number; Probabilistic models; Satellite broadcasting; Statistical inference; Statistics; Synthetic data; synthetic data generator; [Formula: see text] clinical trials; hypertrophic cardiomyopathy; Gaussian Mixture Models; synthetic data generator; computational complexity
• ISSN: 2644-1276; 2644-1276
• DOI: 10.1109/OJEMB.2022.3181796

Goal : To develop a computationally efficient and unbiased synthetic data generator for large-scale in silico clinical trials (CTs). Methods: We propose the BGMM-OCE, an extension of the conventional BGMM (Bayesian Gaussian Mixture Models) algorithm to provide unbiased estimations regarding the optimal number of Gaussian components and yield high-quality, large-scale synthetic data at reduced computational complexity. Spectral clustering with efficient eigenvalue decomposition is applied to estimate the hyperparameters of the generator. A case study is conducted to compare the performance of BGMM-OCE against four straightforward synthetic data generators for in silico CTs in hypertrophic cardiomyopathy (HCM). Results: The BGMM-OCE generated 30000 virtual patient profiles having the lowest coefficient-of-variation (0.046), inter- and intra-correlation differences (0.017, and 0.016, respectively) with the real ones in reduced execution time. Conclusions: BGMM-OCE overcomes the lack of population size in HCM which obscures the development of targeted therapies and robust risk stratification models.