EHR based Genetic Testing Knowledge Base (iGTKB) Development. Issue 4 (December 2015)

• Record Type: Journal Article
• Authors: Zhu, Qian; Liu, Hongfang; Chute, Christopher; Ferber, Matthew
• Is Part Of: BMC medical informatics and decision making. Volume 15:Issue 4(2015)
• Page Start: 1
• Page End: 11
• Publication Date: 2015-12
• Subjects: Clinical feature -- Genetic disorder -- Genetic test -- EHR -- iGTKB; Medical informatics -- Periodicals; Clinical medicine -- Decision making -- Periodicals; 610.285
• DOI: 10.1186/1472-6947-15-S4-S3
• Languages: English
• ISSNs: 1472-6947

Abstract

Abstract Background The gap between a large growing number of genetic tests and a suboptimal clinical workflow of incorporating these tests into regular clinical practice poses barriers to effective reliance on advanced genetic technologies to improve quality of healthcare. A promising solution to fill this gap is to develop an intelligent genetic test recommendation system that not only can provide a comprehensive view of genetic tests as education resources, but also can recommend the most appropriate genetic tests to patients based on clinical evidence. In this study, we developed an EHR based Genetic Testing Knowledge Base for Individualized Medicine (iGTKB). Methods We extracted genetic testing information and patient medical records from EHR systems at Mayo Clinic. Clinical features have been semi-automatically annotated from the clinical notes by applying a Natural Language Processing (NLP) tool, MedTagger suite. To prioritize clinical features for each genetic test, we compared odds ratio across four population groups. Genetic tests, genetic disorders and clinical features with their odds ratios have been applied to establish iGTKB, which is to be integrated into the Genetic Testing Ontology (GTO). Results Overall, there are five genetic tests operated with sample size greater than 100 in 2013 at Mayo Clinic. A total of 1, 450 patients who was tested by one of the five genetic tests have been selected. We assembled 243 clinical features from the Human Phenotype Ontology (HPO) for these five genetic tests. There are 60 clinical features with at least one mention in clinical notes of patients taking the test. Twenty-eight clinical features with high odds ratio (greater than 1) have been selected as dominant features and deposited into iGTKB with their associated information about genetic tests and genetic disorders. Conclusions In this study, we developed an EHR based genetic testing knowledge base, iGTKB. iGTKB will be integrated into the GTO by providing relevant clinical evidence, and ultimately to support development of genetic testing recommendation system, iGenetics.