Using automated text classification to explore uncertainty in NICE appraisals for drugs for rare diseases
Lea Wiedmann,
Jack Blumenau,
Orlagh Carroll
et al.
Abstract:Objective: This study examined the application, feasibility, and validity of supervised learning models for text classification in appraisals for rare disease treatments (RDTs) in relation to uncertainty, and analyzed differences between appraisals based on the classification results. Methods: We analyzed appraisals for RDTs (n = 94) published by the National Institute for Health and Care Excellence (NICE) between January 2011 and May 2023. We used Naïve Bayes, Lasso, and Support Vector Machine models in a bin… Show more
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