Idioma:

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

Pezoulas, Vasileios C ; Tachos, Nikolaos S ; Gkois, George ; Olivotto, Iacopo ; Barlocco, Fausto ; Fotiadis, Dimitrios I

Engineering in Medicine and Biology, IEEE Open Journal of, 2022, Vol.3, p.108-114

IEEE

Sem texto completo

Citações Citado por

Enviar para

Título:
Bayesian Inference-Based Gaussian Mixture Models With Optimal Components Estimation Towards Large-Scale Synthetic Data Generation for In Silico Clinical Trials
Autor: Pezoulas, Vasileios C ; Tachos, Nikolaos S ; Gkois, George ; Olivotto, Iacopo ; Barlocco, Fausto ; Fotiadis, Dimitrios I
Assuntos: <; 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 ; Cardiology ; Clinical diagnosis ; computational complexity ; Computational modeling ; Correlation ; Estimation ; Gaussian Mixture Models ; Gaussian processes ; Generators ; hypertrophic cardiomyopathy ; Satellite broadcasting ; Statistics ; synthetic data generator
É parte de: Engineering in Medicine and Biology, IEEE Open Journal of, 2022, Vol.3, p.108-114
Descrição: 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.
Editor: IEEE
Idioma: Inglês

Voltar para lista de resultados

Resultado 1 Avançar Ir para próxima página

Realização: Logos de Redes Sociais:

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

Pezoulas, Vasileios C ; Tachos, Nikolaos S ; Gkois, George ; Olivotto, Iacopo ; Barlocco, Fausto ; Fotiadis, Dimitrios I

IEEE

Buscando em bases de dados remotas. Favor aguardar.