A Machine Learning Approach for Quality Assurance of SNOMED CT Interoperability of health information has been declared as a national objective to be achieved using Electronic Health Records (EHRs). The HITECH Act of 2009 in the US helped facilitate the adoption of EHRs by authorizing incentive payments through Medicare and Medicaid to hospitals and clinicians for using EHRs to achieve improvements in healthcare delivery. SNOMED CT, being a global standardized terminology and a requirement for the meaningful use of EHRs, will play an integral role in achieving widespread exchange of clinical information and decision support tools. However, quality issues with SNOMED CT may hinder the progress that we have seen with the adoption of EHRs at hospitals and clinics. Therefore, it becomes essential that efficient methods be formulated to assure the quality of SNOMED CT. This study presents a machine learning approach using a contextbased, lexical method. The results show that such computational methods can effectively be used to identify problematic areas in SNOMED CT where the limited auditing resources could then be allocated.

Testing and Evaluating SNOMED CT Web Browsers Textual Search Feature SNOMED CT is the comprehensive medical and clinical vocabulary populated worldwide. With rapid increasing of the scale, it requires powerful and userfriendly tools for data retrieving. The browsers should not only provide accurate retrieval of concepts, but also support approximately searching on their textual search function. In this paper, we evaluate seven SNOMED CT web browsers on their textual search feature. Our evaluation process consists of three steps:1) designing test cases; 2) using test cases to examine browsersu0027 textual search feature; and 3) analyzing the search results using Precision u0026 Recall, Fscore, and Normalized Discounted Cumulative Gain(nDCG). The performance report for these seven browsers is provided. In the paper, we also discuss the possibility of optimizing SNOMED CT browsersu0027 search function by using ontology deep learning model xe2x80x90xe2x80x90 Deep Knowledge Extraction from Text (DKET).