title: Automatic Classification of Structured Product Labels for Pregnancy Risk Drug Categories, a Machine Learning Approach.
abstract: With regular expressions and manual review, 18,342 FDA-approved drug product labels were processed to determine if the five standard pregnancy drug risk categories were mentioned in the label. After excluding 81 drugs with multiple-risk categories, 83% of the labels had a risk category within the text and 17% labels did not. We trained a Sequential Minimal Optimization algorithm on the labels containing pregnancy risk information segmented into standard document sections. For the evaluation of the classifier on the testing set, we used the Micromedex drug risk categories. The precautions section had the best performance for assigning drug risk categories, achieving Accuracy 0.79, Precision 0.66, Recall 0.64 and F1 measure 0.65. Missing pregnancy risk categories could be suggested using machine learning algorithms trained on the existing publicly available pregnancy risk information.
authors.affiliation: Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD.
authors.forename: Laritza M
authors.initials: LM
authors.lastname: Rodriguez
authors.affiliation: Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD.
authors.forename: Dina Demner
authors.initials: DD
authors.lastname: Fushman
mesh_terms: D000465:Algorithms; D004348:Drug Labeling; D005260:Female; D006801:Humans; D000069550:Machine Learning; D011247:Pregnancy; D011248:Pregnancy Complications; D012306:Risk
keywords: data-mining; document classification; drug risk; knowledge extraction; machine learning; pregnancy