Intelligent Educational Data Analysis with Gaussian Processes

Wang, Jiachun; Zhao, Jing; Sun, Shiliang; Shi, Dongyu

doi:10.1007/978-3-030-04224-0_30

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…We also mentioned in the discussion the convenience of exploiting the parallelization potential of the different areas of the optimization process. For that purpose we use the Particle Swarm method for the optimization of the acquisition function, but other more sophisticated venues for parallelization include batched optimization [19], parallel acquisition functions [20] or GPU approaches [21] among others.…”

Section: Future Work and Conclusionmentioning

confidence: 99%

Bayesian Optimization with Additive Kernels for the Calibration of Simulation Models to Perform Cost-Effectiveness Analysis

Gómez-Guillén,

Díaz,

Arcos

et al. 2023

Frontiers in Artificial Intelligence and Applications

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The use of mathematical simulation models of diseases in economic evaluation is an essential and common tool in medicine aimed at guiding decision-making in health. Cost-effectiveness analyses are a type of economic evaluation that assess the balance between health benefits and the economic sustainability of different health interventions. One critical aspect of these models is the accurate representation of the disease’s natural history, which requires a set of parameters such as probabilities and disease burden rates. While these parameters can be obtained from scientific literature, they often need calibration to fit the model’s expected outcomes. However, the calibration process can be computationally expensive and traditional optimization methods can be time-consuming due to relatively simple heuristics that may not even guarantee feasible solutions. In this work, we investigate the use of Bayesian optimization to enhance the calibration process by leveraging domain-specific knowledge and exploiting inherent structural properties in the solution space. Specifically, we examine the effect of additive kernel decomposition and constraint handling for efficient search. Our preliminary results show that this improved Bayesian optimization procedure asymptotically improves the calibration process, leading to faster convergence and better solutions for larger simulation models.

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Section: Future Work and Conclusionmentioning

confidence: 99%

Bayesian Optimization with Additive Kernels for the Calibration of Simulation Models to Perform Cost-Effectiveness Analysis

Gómez-Guillén,

Díaz,

Arcos

et al. 2023

Frontiers in Artificial Intelligence and Applications

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show abstract

“…GP regression has OðN 3 Þ memory and OðN 2 Þ time complexity, where N is the number of observations, 17 which is perhaps the main reason why it has not been widely adopted in clinical imaging. Recent work 20 has pushed exact GP training to over one million data points using multi-GPU parallelization and methods such as linear conjugate gradients. However, medical images can contain tens of millions of pixels or voxels and medical applications generally require real-time performance.…”

Section: Reducing Computational Cost-patched Gp Regressionmentioning

confidence: 99%

Gaussian process regression for ultrasound scanline interpolation

2022

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Purpose: In ultrasound imaging, interpolation is a key step in converting scanline data to brightness-mode (B-mode) images. Conventional methods, such as bilinear interpolation, do not fully capture the spatial dependence between data points, which leads to deviations from the underlying probability distribution at the interpolation points.Approach: We propose Gaussian process (GP) regression as an improved method for ultrasound scanline interpolation. Using ultrasound scanlines acquired from two different ultrasound scanners during in vivo trials, we compare the scanline conversion accuracy of three standard interpolation methods with that of GP regression, measuring the peak signal-to-noise ratio (PSNR) and mean absolute error (MAE) for each method. Results:The PSNR and MAE scores show that GP regression leads to more accurate scanline conversion compared to the nearest neighbor, bilinear, and cubic spline interpolation methods, for both datasets. Furthermore, limiting the interpolation window size of GP regression to 15 reduces computation time with minimal to no reduction in PSNR.Conclusions: GP regression quantitatively leads to more accurate scanline conversion and provides uncertainty estimates at each of the interpolation points. Our windowing method reduces the computational cost of using GP regression for scanline conversion.

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Intelligent Educational Data Analysis with Gaussian Processes

Cited by 2 publications

References 9 publications

Bayesian Optimization with Additive Kernels for the Calibration of Simulation Models to Perform Cost-Effectiveness Analysis

Bayesian Optimization with Additive Kernels for the Calibration of Simulation Models to Perform Cost-Effectiveness Analysis

Gaussian process regression for ultrasound scanline interpolation

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