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

Ruxandra Stoean, Catalin Stoean, Miguel Atencia, Roberto Rodríguez-Labrada and Gonzalo Joya
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Ruxandra Stoean: Romanian Institute of Science and Technology, 400022 Cluj-Napoca, Romania
Catalin Stoean: Romanian Institute of Science and Technology, 400022 Cluj-Napoca, Romania
Miguel Atencia: Department of Applied Mathematics, Universidad de Málaga, 29071 Málaga, Spain
Roberto Rodríguez-Labrada: Cuban Neuroscience Center, 11600 Havana, Cuba
Gonzalo Joya: Department of Electronic Technology, Universidad de Málaga, 29071 Málaga, Spain

Mathematics, 2020, vol. 8, issue 7, 1-17

Abstract: 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 average probability and variance for each diagnostic class of the problem. The current paper puts forward a convolutional–long short-term memory network model with a Monte Carlo dropout layer for obtaining information regarding the model uncertainty for saccadic records of all patients. These are next used in assessing the uncertainty of the learning model at the higher level of sets of multiple records (i.e., registers) that are gathered for one patient case by the examining physician towards an accurate diagnosis. Means and standard deviations are additionally calculated for the Monte Carlo uncertainty estimates of groups of predictions. These serve as a new collection where a random forest model can perform both classification and ranking of variable importance. The approach is validated on a real-world problem of classifying electrooculography time series for an early detection of spinocerebellar ataxia 2 and reaches an accuracy of 88.59% in distinguishing between the three classes of patients.

Keywords: deep learning; time series; uncertainty quantification; Monte Carlo dropout; random forest (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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