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Uncertainty

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Neural parameter calibration and uncertainty quantification for epidemic forecasting.

PloS one
The recent COVID-19 pandemic has thrown the importance of accurately forecasting contagion dynamics and learning infection parameters into sharp focus. At the same time, effective policy-making requires knowledge of the uncertainty on such prediction...

Refined matrix completion for spectrum estimation of heart rate variability.

Mathematical biosciences and engineering : MBE
Heart rate variability (HRV) is an important metric in cardiovascular health monitoring. Spectral analysis of HRV provides essential insights into the functioning of the cardiac autonomic nervous system. However, data artefacts could degrade signal q...

The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education.

JMIR medical education
In the field of medicine, uncertainty is inherent. Physicians are asked to make decisions on a daily basis without complete certainty, whether it is in understanding the patient's problem, performing the physical examination, interpreting the finding...

Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks.

Science advances
Random noise in information processing systems is widely seen as detrimental to function. But despite the large trial-to-trial variability of neural activity, humans show a remarkable adaptability to conditions with uncertainty during goal-directed b...

Uncertainty Qualification for Deep Learning-Based Elementary Reaction Property Prediction.

Journal of chemical information and modeling
The prediction of the thermodynamic and kinetic properties of elementary reactions has shown rapid improvement due to the implementation of deep learning (DL) methods. While various studies have reported the success in predicting reaction properties,...

Enhancing long-term water quality modeling by addressing base demand, demand patterns, and temperature uncertainty using unsupervised machine learning techniques.

Water research
Water quality modelling in Water Distribution systems (WDS) is frequently affected by uncertainties in input variables such as base demand and decay constants. When utilizing simulation tools like EPANET, which necessitate exact numerical inputs, the...

Uncertainty guided semi-supervised few-shot segmentation with prototype level fusion.

Neural networks : the official journal of the International Neural Network Society
Few-Shot Semantic Segmentation (FSS) aims to tackle the challenge of segmenting novel categories with limited annotated data. However, given the diversity among support-query pairs, transferring meta-knowledge to unseen categories poses a significant...

HAGMN-UQ: Hyper association graph matching network with uncertainty quantification for coronary artery semantic labeling.

Medical image analysis
Coronary artery disease (CAD) is one of the leading causes of death worldwide. Accurate extraction of individual arterial branches from invasive coronary angiograms (ICA) is critical for CAD diagnosis and detection of stenosis. Generating semantic se...

Unraveling Uncertainty: The Impact of Biological and Analytical Variation on the Prediction Uncertainty of Categorical Prediction Models.

The journal of applied laboratory medicine
BACKGROUND: Interest in prediction models, including machine learning (ML) models, based on laboratory data has increased tremendously. Uncertainty in laboratory measurements and predictions based on such data are inherently intertwined. This study d...

Uncertainty-Aware Deep Learning Characterization of Knee Radiographs for Large-Scale Registry Creation.

The Journal of arthroplasty
BACKGROUND: We present an automated image ingestion pipeline for a knee radiography registry, integrating a multilabel image-semantic classifier with conformal prediction-based uncertainty quantification and an object detection model for knee hardwar...