Ranking Information Extracted from Uncertainty Quantification of the Prediction of a Deep Learning Model on Medical Time Series Data

Ranking Information Extracted from Uncertainty Quantification of the Prediction of a Deep Learning Model on Medical Time Series Data

Uncertainty quantification in deep learning models is especially important for the medical applications of this complex and successful type of neural architectures. One popular technique is Monte Carlo dropout that gives a sample output for a record, which can be measured statistically in terms of a...

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Bibliographic Details
Main Authors: Ruxandra Stoean, Catalin Stoean, Miguel Atencia, Roberto Rodríguez-Labrada, Gonzalo Joya
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Mathematics
Subjects:
deep learning
time series
uncertainty quantification
Monte Carlo dropout
random forest
Online Access:https://www.mdpi.com/2227-7390/8/7/1078

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