INTRODUCTION: Risk prediction models are increasingly used in healthcare to aid in clinical decision-making. In most clinical contexts, model calibration (i.e., assessing the reliability of risk estimates) is critical. Data available for model develo...
Heterogeneous treatment effect estimation is an important problem in precision medicine. Specific interests lie in identifying the differential effect of different treatments based on some external covariates. We propose a novel non-parametric treatm...
Soil pH is an important parameter that affects plant nutrient uptake and biological activity and has received extensive attention and research. In this paper, we propose a neural network algorithm using Ghostnet combined with Convolutional Block Atte...
Enhancement prediction of load demand is crucial for effective energy management and resource allocation in modern power systems and especially in medical segment. Proposed method leverages strengths of ANFIS in learning complex nonlinear relationshi...
Molecular dynamics simulations are crucial for understanding the structural and dynamical behavior of biomolecular systems, including the impact of their environment. However, there is a gap between the time scale of these simulations and that of rea...
Many human diseases result from a complex interplay of behavioral, clinical, and molecular factors. Integrating low-dimensional behavioral and clinical features with high-dimensional molecular profiles can significantly improve disease outcome predic...
This paper focuses on the neutron spectrum measurement using a liquid scintillation detector, where the neutron spectrum could be identified and unfolded from the light output distribution of the EJ-301 liquid scintillation detector through a linear ...
Cosmic radiation exposure is one of the important health concerns for aircrews. In this work, we constructed a back propagation neural network model for the real-time and rapid assessment of cosmic radiation exposure to the public in aviation. The mu...
Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in pred...
Journal of X-ray science and technology
Jan 1, 2024
PURPOSE: This study aims to propose and develop a fast, accurate, and robust prediction method of patient-specific organ doses from CT examinations using minimized computational resources.
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