Targeting the uncertainty of predictions at patient-level using an ensemble of classifiers coupled with calibration methods, Venn-ABERS, and Conformal Predictors: A case study in AD

Volume: 101, Pages: 103350 - 103350
Published: Jan 1, 2020
Abstract
Despite being able to make accurate predictions, most existing prognostic models lack a proper indication about the uncertainty of each prediction, that is, the risk of prediction error for individual patients. This hampers their translation to primary care settings through decision support systems. To address this problem, we studied different methods for transforming classifiers into probabilistic/confidence-based predictors (here called...
Paper Details
Title
Targeting the uncertainty of predictions at patient-level using an ensemble of classifiers coupled with calibration methods, Venn-ABERS, and Conformal Predictors: A case study in AD
Published Date
Jan 1, 2020
Volume
101
Pages
103350 - 103350
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