Uri Kartoun presenting EMRBots at Stanford University, Feb. 2019.
EMRBots are experimental artificially generated
electronic medical records (EMRs).[1][2] The aim of EMRBots is to allow non-commercial entities (such as universities) to use the artificial patient repositories to practice statistical and machine-learning algorithms. Commercial entities can also use the repositories for any purpose, as long as they do not create software products using the repositories.
A letter published in
Communications of the ACM emphasizes the importance of using synthetic medical data, "... EMRBots can generate a synthetic patient population of any size, including demographics, admissions, comorbidities, and laboratory values. A synthetic patient has no confidentiality restrictions and thus can be used by anyone to practice machine learning algorithms."[3]
Background
EMRs contain sensitive personal information. For example, they may include details about infectious diseases, such as
human immunodeficiency virus (HIV), or they may contain information about a
mental disorder. They may also contain other sensitive information such as medical details related to fertility treatments. Because EMRs are subject to confidentiality requirements, accessing and analyzing EMR databases is a privilege given to only a small number of individuals. Individuals who work at institutions that do not have access to EMR systems have no opportunity to gain hands-on experience with this valuable resource. Simulated medical databases are currently available; however, they are difficult to configure and are limited in their resemblance to real clinical databases. Generating highly accessible repositories of artificial patient EMRs while relying only minimally on real patient data is expected to serve as a valuable resource to a broader audience of medical personnel, including those who reside in underdeveloped countries.
Use in Schools
In March 2022, Ishani Das, a student researcher from Cupertino High School, used EMRBots to develop an Artificial Intelligence based Clinical Decision Support Tool which is available via the open-source community
AI-Assist.[4]
In March 2019 the repositories were used to enhance "Computationally-Enabled Medicine", a course given by Harvard Medical School.[47] Further in March, scientists from multiple institutions, including
Peking University,
University of Tokyo, and
Polytechnic University of Milan used the repositories to develop a new framework focused on medical information privacy.[48]
In May 2018
Northwell Health funded a project denoted as EMRBot in the health system's third annual innovation challenge.
Northwell Health's EMRBot, however, is neither related to Uri Kartoun's website (registered as a domain name in April 2015; www.emrbots.org) nor to any of its repositories or applications.
Criticism
"[EMRBots] are ... pregenerated datasets of synthetic EHR with an insufficient explanation of how the datasets were generated. These datasets exhibit several inconsistencies between health problems, age, and gender."[62][63] An additional criticism is described in a thesis ("Realism in Synthetic Data Generation") granted by
Massey University.[64]
^Ma, Tengfei; Xiao, Cao; Wang, Fei (2018). "Health-ATM: A Deep Architecture for Multifaceted Patient Health Record Representation and Risk Prediction". Proceedings of the 2018 SIAM International Conference on Data Mining. pp. 261–269.
doi:
10.1137/1.9781611975321.30.
ISBN978-1-61197-532-1.
^Multidimensional Group Recommendations in the Health Domain
^Satti, Fahad Ahmed; Ali Khan, Wajahat; Ali, Taqdir; Hussain, Jamil; Yu, Hyeong Won; Kim, Seoungae; Lee, Sungyoung (2020). "Semantic Bridge for Resolving Healthcare Data Interoperability". 2020 International Conference on Information Networking (ICOIN). pp. 86–91.
doi:
10.1109/ICOIN48656.2020.9016461.
ISBN978-1-7281-4199-2.
S2CID212634693.
^Gebert, Theresa; Jiang, Shuli; Sheng, Jiaxian (2018). "Characterizing Allegheny County opioid overdoses with an interactive data explorer and synthetic prediction tool".
arXiv:1804.08830 [
stat.AP].