AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Uncertainty

Showing 501 to 510 of 667 articles

Clear Filters

Barrier Function-Based Adaptive Control for Uncertain Strict-Feedback Systems Within Predefined Neural Network Approximation Sets.

IEEE transactions on neural networks and learning systems
In this article, a globally stable adaptive control strategy for uncertain strict-feedback systems is proposed within predefined neural network (NN) approximation sets, despite the presence of unknown system nonlinearities. In contrast to the convent...

Range and dose verification in proton therapy using proton-induced positron emitters and recurrent neural networks (RNNs).

Physics in medicine and biology
Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution and the activity distr...

Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models.

Computers in biology and medicine
One of the significant issues in global healthcare systems is improving the supply chain performance and addressing the uncertainties in demand. Blood products, especially platelets, have the most challenging supply chains in the health system given ...

Predicting gamma passing rates for portal dosimetry-based IMRT QA using machine learning.

Medical physics
PURPOSE: Intensity-modulated radiation therapy (IMRT) quality assurance (QA) measurements are routinely performed prior to treatment delivery to verify dose calculation and delivery accuracy. In this work, we applied a machine learning-based approach...

Accelerating cardiovascular model building with convolutional neural networks.

Medical & biological engineering & computing
The objective of this work is to reduce the user effort required for 2D segmentation when building patient-specific cardiovascular models using the SimVascular cardiovascular modeling software package. The proposed method uses a fully convolutional n...

A Natural-language-based Visual Query Approach of Uncertain Human Trajectories.

IEEE transactions on visualization and computer graphics
Visual querying is essential for interactively exploring massive trajectory data. However, the data uncertainty imposes profound challenges to fulfill advanced analytics requirements. On the one hand, many underlying data does not contain accurate ge...

Efficient training of interval Neural Networks for imprecise training data.

Neural networks : the official journal of the International Neural Network Society
This paper describes a robust and computationally feasible method to train and quantify the uncertainty of Neural Networks. Specifically, we propose a back propagation algorithm for Neural Networks with interval predictions. In order to maintain nume...

Delay-dependent stability analysis of the QUAD vector field fractional order quaternion-valued memristive uncertain neutral type leaky integrator echo state neural networks.

Neural networks : the official journal of the International Neural Network Society
This paper studies the robust stability analysis for a class of memristive-based neural networks (NN). The NN consists of a fractional order neutral type quaternion-valued leaky integrator echo state with parameter uncertainties and time-varying dela...

Comprehensive Eutrophication Assessment Based on Fuzzy Matter Element Model and Monte Carlo-Triangular Fuzzy Numbers Approach.

International journal of environmental research and public health
Evaluating the eutrophication level of lakes with a single method alone is challenging since uncertain, fuzzy, and complex processes exist in eutrophication evaluations. The parameters selected for assessing eutrophication include chlorophyII-a, chem...

Confidence resets reveal hierarchical adaptive learning in humans.

PLoS computational biology
Hierarchical processing is pervasive in the brain, but its computational significance for learning under uncertainty is disputed. On the one hand, hierarchical models provide an optimal framework and are becoming increasingly popular to study cogniti...