A bottom-up approach to creating an ontology for medication indications. (23rd January 2021)

• Record Type: Journal Article
• Authors: Nelson, Stuart J; Flynn, Allen; Tuttle, Mark S
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 4(2021)
• Page Start: 753
• Page End: 758
• Publication Date: 2021-01-23
• Subjects: drug information services -- information management -- information storage and retrieval -- medication indications; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa331
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objectives: The study sought to learn if it were possible to develop an ontology that would allow the Food and Drug Administration approved indications to be expressed in a manner computable and comparable to what is expressed in an electronic health record. Materials and Methods: A random sample of 1177 of the 3000+ extant, distinct medical products (identified by unique new drug application numbers) was selected for investigation. Close manual examination of the indication portion of the labels for these drugs led to the development of a formal model of indications. Results: The model represents each narrative indication as a disjunct of conjuncts of assertions about an individual. A desirable attribute is that each assertion about an individual should be testable without reference to other contextual information about the situation. The logical primitives are chosen from 2 categories (context and conditions) and are linked to an enumeration of uses, such as prevention. We found that more than 99% of approved label indications for treatment or prevention could be so represented. Discussion: While some indications are straightforward to represent, difficulties stem from the need to represent temporal or sequential references. In addition, there is a mismatch of terminologies between what is present in an electronic health record and in the label narrative. Conclusions: A workable model for formalizing drug indications is possible. Remaining challenges include designing workflow to model narrative label indications for all approved drug products and incorporation of standard vocabularies.