IDISK: the integrated DIetary Supplements Knowledge base. (18th February 2020)

• Record Type: Journal Article
• Authors: Rizvi, Rubina F; Vasilakes, Jake; Adam, Terrence J; Melton, Genevieve B; Bishop, Jeffrey R; Bian, Jiang; Tao, Cui; Zhang, Rui
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 4(2020)
• Page Start: 539
• Page End: 548
• Publication Date: 2020-02-18
• Subjects: dietary supplements -- knowledge representation -- terminology -- RxNorm -- unified medical language system; 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/ocz216
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: To build a knowledge base of dietary supplement (DS) information, called the integrated DIetary Supplement Knowledge base (iDISK), which integrates and standardizes DS-related information from 4 existing resources. Materials and Methods: iDISK was built through an iterative process comprising 3 phases: 1) establishment of the content scope, 2) development of the data model, and 3) integration of existing resources. Four well-regarded DS resources were integrated into iDISK: The Natural Medicines Comprehensive Database, the "About Herbs" page on the Memorial Sloan Kettering Cancer Center website, the Dietary Supplement Label Database, and the Natural Health Products Database. We evaluated the iDISK build process by manually checking that the data elements associated with 50 randomly selected ingredients were correctly extracted and integrated from their respective sources. Results: iDISK encompasses a terminology of 4208 DS ingredient concepts, which are linked via 6 relationship types to 495 drugs, 776 diseases, 985 symptoms, 605 therapeutic classes, 17 system organ classes, and 137 568 DS products. iDISK also contains 7 concept attribute types and 3 relationship attribute types. Evaluation of the data extraction and integration process showed average errors of 0.3%, 2.6%, and 0.4% for concepts, relationships and attributes, respectively. Conclusion: We developed iDISK, a publicly available standardized DS knowledge base that can facilitate more efficient and meaningful dissemination of DS knowledge.