iDISK: the integrated DIetary Supplements Knowledge base

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.

[1]  Rui Zhang,et al.  Term Coverage of Dietary Supplements Ingredients in Product Labels , 2016, AMIA.

[2]  I. Sarkar,et al.  Identifying Supplement Use Within Clinical Notes: An Applicationof Natural Language Processing , 2018, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

[3]  Ly Le,et al.  Semantic Relation Extraction for Herb-Drug Interactions from the Biomedical Literature Using an Unsupervised Learning Approach , 2018, 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE).

[4]  E. Raschi,et al.  Adverse reactions to dietary supplements containing red yeast rice: assessment of cases from the Italian surveillance system , 2017, British journal of clinical pharmacology.

[5]  Terrence Adam,et al.  Evaluation of Herbal and Dietary Supplement Resource Term Coverage , 2015, MedInfo.

[6]  Indra Neil Sarkar,et al.  Identifying natural health product and dietary supplement information within adverse event reporting systems , 2018, PSB.

[7]  Georgeta Bordea,et al.  Enabling West African Herbal-Based Traditional Medicine Digitizing: The WATRIMed Knowledge Graph , 2019, MedInfo.

[8]  Cui Tao,et al.  Toward a normalized clinical drug knowledge base in China—applying the RxNorm model to Chinese clinical drugs , 2018, J. Am. Medical Informatics Assoc..

[9]  Hanspeter Pfister,et al.  UpSet: Visualization of Intersecting Sets , 2014, IEEE Transactions on Visualization and Computer Graphics.

[10]  Abeed Sarker,et al.  Finding Potentially Unsafe Nutritional Supplements from User Reviews with Topic Modeling , 2016, PSB.

[11]  Christophe G. Lambert,et al.  Bridging Islands of Information to Establish an Integrated Knowledge Base of Drugs and Health Outcomes of Interest , 2014, Drug Safety.

[12]  H. Scheepers,et al.  Should women be advised to use calcium supplements during pregnancy? A decision analysis , 2018, Maternal & child nutrition.

[13]  Anders Møller,et al.  A structured vocabulary for indexing dietary supplements in databases in the United States. , 2012, Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems.

[14]  J. Dwyer,et al.  Why Americans Need Information on Dietary Supplements. , 2018, The Journal of nutrition.

[15]  Rui Zhang,et al.  Comparing Existing Resources to Represent Dietary Supplements , 2018, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

[16]  Reed McEwan,et al.  Using word embeddings to expand terminology of dietary supplements on clinical notes , 2019, JAMIA open.

[17]  Nadine Shehab,et al.  Emergency Department Visits for Adverse Events Related to Dietary Supplements. , 2015, New England Journal of Medicine.

[18]  Fleur Mougin,et al.  Romedi: An Open Data Source About French Drugs on the Semantic Web , 2019, MedInfo.

[19]  Rui Zhang,et al.  Using natural language processing methods to classify use status of dietary supplements in clinical notes , 2018, BMC Medical Informatics and Decision Making.

[20]  J. Cimino.  Desiderata for Controlled Medical Vocabularies in the Twenty-First Century , 1998, Methods of Information in Medicine.

[21]  Yves A. Lussier,et al.  Word-of-Mouth Innovation: Hypothesis Generation for Supplement Repurposing based on Consumer Reviews , 2017, AMIA.

[22]  Joseph M. Betz,et al.  Dietary supplement use in the United States, 2003-2006. , 2011, The Journal of nutrition.

[23]  J. Friedman,et al.  Diagnoses associated with dietary supplement use in a national dataset. , 2019, Complementary therapies in medicine.

[24]  Olivier Bodenreider,et al.  The Unified Medical Language System (UMLS): integrating biomedical terminology , 2004, Nucleic Acids Res..