Advancing Informatics with Electronic Medical Records Bots

Speaker:  Uri Kartoun – Cambridge, MA, United States
Topic(s):  Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science

Abstract

Electronic medical records (EMRs) contain sensitive and detailed documentation on a variety of conditions at the individual level. Because EMRs are subject to confidentiality requirements, access to them is limited, thus preventing most scientists from gaining hands-on experience with this valuable resource. In an attempt to address such limitations, I developed software that generates experimental artificially generated electronic medical records (EMRBots) [1, 2]. Since becoming publicly accessible in April 2015, EMRBot repositories have been used to advance the field of computer science as well as its subdomain, health informatics. For instance, the repositories have been used to develop a neural network superior to the widely used long short-term memory neural network. The repositories have also been used in a variety of scenarios to advance teaching, enhance student dissertations, facilitate hackathons, and produce R packages. In my lecture I will introduce EMRBots and discuss potential use cases, including the concept of extending Turing Test when applied to health care data [3]. 

1. EMRBots Wikipedia page. https://en.wikipedia.org/wiki/EMRBots

2. Kartoun U. Workshop at Princeton University (HackPrinceton). Nov. 10, 2018.

3. Kartoun U. and CACM Staff. A leap from artificial to intelligence. Communications of the ACM 2018;61(1):10–11. https://dl.acm.org/citation.cfm?id=3176926.3168260                                                                                                             

About this Lecture

Number of Slides:  30
Duration:  45 minutes
Languages Available:  English
Last Updated: 

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