Using automated text classification to explore uncertainty in NICE appraisals for drugs for rare diseases.

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 binary text classification task (classifying paragraphs as either referencing uncertainty in the evidence base or not). To illustrate the results, we tested hypotheses in relation to the appraisal guidance, advanced therapy medicinal product (ATMP) status, disease are

SUBMITTER: Wiedmann L 

PROVIDER: S-EPMC10859832 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature