https://www.selleckchem.com/products/mk-0752.html SNOMED CT is a comprehensive and evolving clinical reference terminology that has been widely adopted as a common vocabulary to promote interoperability between Electronic Health Records. Owing to its importance in healthcare, quality assurance becomes an integral part of the lifecycle of SNOMED CT. While, manual auditing of every concept in SNOMED CT is difficult and labor intensive, identifying inconsistencies in the modeling of concepts without any context can be challenging. Algorithmic techniques are needed to identify modeling inconsistencies, if any, in SNOMED CT. This study proposes a context-based, machine learning quality assurance technique to identify concepts in SNOMED CT that may be in need of auditing. The Clinical Finding and the Procedure hierarchies are used as a testbed to check the efficacy of the method. Results of auditing show that the method identified inconsistencies in 72% of the concept pairs that were deemed inconsistent by the algorithm. The method is shown to be effective in both maximizing the yield of correction, as well as providing a context to identify the inconsistencies. Such methods, along with SNOMED International's own efforts, can greatly help reduce inconsistencies in SNOMED CT.Driving is a complex task that consists of several physical (motor-related) and physiological (biological changes within the body) processes occurring simultaneously. The complexity of the task depends on several factors, but this research focuses on work zone configurations and their effect on driver performance and gaze behavior. The increase in work zone fatalities in the United States between 2015 and 2018 coupled with the limited literature of driver behavior in these complex environments requires a more comprehensive study. Given the nature of these crashes, typically lane departures, gaze behavior provided an additional physiological dimension to the present research. A framework that comprises