From Wikipedia, the free encyclopedia
Jump to navigation Jump to search
Uri Kartoun presenting EMRBots at Stanford University, Feb. 2019.

EMRBots are experimental artificially generated electronic medical records (EMRs).[1] 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."[2]


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.

Academic use[edit]

In April 2018 Bioinformatics (journal) published a study that relied on EMRBots data to create a new R package denoted as "comoRbidity".[3][4] Co-authors on the study included scientists from Universitat Pompeu Fabra and Harvard University. The repositories have been used to accelerate research, e.g., researchers from Michigan State University, IBM Research, and Cornell University published a study in the Knowledge Discovery and Data Mining (KDD) conference.[5][6][7][8] Their study describes a novel neural network that performs better than the widely used long short-term memory neural network developed by Sepp Hochreiter and Jürgen Schmidhuber in 1997.[9] In May 2018 scientists from IBM Research and Cornell University have used the repositories to test a new deep architecture denoted as Health-ATM. To demonstrate superiority over traditional neural networks, they applied their architecture to a congestive heart failure use case.[10] Additional use includes The University of Chicago,[11] University of California Merced,[12][13] and The University of Tampere, Finland.[14][15] Additional resources include.[16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]

In March 2019 the repositories were used to enhance "Computationally-Enabled Medicine", a course given by Harvard Medical School.[36] 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[37]

Use in hackathons[edit]

Researchers from Carnegie Mellon University used EMRBots data at the CMU HackAuton hackathon to create a prediction tool.[38] Additional uses are available.[39]

EMRBots were presented at HackPrinceton 2018 organized by Princeton University.[40][41][42]

EMRBots were presented at TreeHacks 2019 organized by Stanford University.[43]


The repositories can be downloaded after registration.[44]

The repositories are available to download from Figshare without registration.[45][46][47]

Full source code for creating the repositories is available to download from Figshare.[48]

Northwell Health's EMRBot[edit]

In May 2018 Northwell Health funded a project denoted as EMRBot in the health system's third annual innovation challenge.[49][50][51][52] [53][54][55][56][57][58][59][60][61][62] 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.


"[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."[63][64] An additional criticism is described in a thesis ("Realism in Synthetic Data Generation") granted by Massey University.[65]


  1. ^ Kartoun, Uri (2016). "A methodology to generate virtual patient repositories". arXiv:1608.00570 [cs.CY].
  2. ^ CACM Staff (2018). "A leap from artificial to intelligence". Communications of the ACM. 60 (1): 10–11. doi:10.1145/3034429.
  3. ^ Gutiérrez-Sacristán, Alba; Bravo, Àlex; Giannoula, Alexia; Mayer, Miguel A.; Sanz, Ferran; Furlong, Laura I. (2018). "comoRbidity: an R package for the systematic analysis of disease comorbidities". Bioinformatics. 34 (18): 3228–3230. doi:10.1093/bioinformatics/bty315. PMC 6137966. PMID 29897411.
  4. ^ Gutiérrez-Sacristán, A; Bravo, À; Giannoula, A; Mayer, MA; Sanz, F; Furlong, LI (2018). "comoRbidity: an R package for the systematic analysis of disease comorbidities". Bioinformatics. 34: 3228–3230. doi:10.1093/bioinformatics/bty315. PMC 6137966. PMID 29897411.
  5. ^ "Patient Subtyping via Time-Aware LSTM Networks". Kdd.org. Retrieved 24 May 2018.
  6. ^ "SIGKDD". Kdd.org. Retrieved 24 May 2018.
  7. ^ http://biometrics.cse.msu.edu/Presentations/InciBaytas_PatientSubtypingViaTimeAwareLSTMNetworks_KDD_2017.pdf
  8. ^ http://biometrics.cse.msu.edu/Publications/Thesis/InciBaytas_ContributionsToMatchineLearningInBiomedicalInformation.pdf
  9. ^ Hochreiter, Sepp; Schmidhuber, Jürgen (1997). "Long short-term memory". Neural Comput. 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735.
  10. ^ 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. ISBN 978-1-61197-532-1.
  11. ^ "Statistical Modeling of Clinical Data" (PDF). Cri.uchicago.edu. Retrieved 24 May 2018.
  12. ^ Bahrami, Mehdi; Singhal, Mukesh (2015). "A dynamic cloud computing platform for eHealth systems". A dynamic cloud computing platform for eHealth systems - IEEE Conference Publication. pp. 435–438. doi:10.1109/HealthCom.2015.7454539. ISBN 978-1-4673-8325-7.
  13. ^ "Publication - UC Merced Cloud Lab". Cloudlab.ucmerced.edu.
  14. ^ "Fairness in Group Recommendations in the Health Domain" (PDF). People.uta.fi. Retrieved 24 May 2018.
  15. ^ "MLARAPP". Devpost.com. Retrieved 24 May 2018.
  16. ^ "illidanlab/T-LSTM". GitHub. Retrieved 24 May 2018.
  17. ^ Stratigi, Maria; Kondylakis, Haridimos; Stefanidis, Kostas (2018). Database and Expert Systems Applications. Lecture Notes in Computer Science. 11030. pp. 147–155. doi:10.1007/978-3-319-98812-2_11. ISBN 978-3-319-98811-5.
  18. ^ "Teaching data science fundamentals through realistic synthetic clinical cardiovascular data". bioRxiv 232611.
  19. ^ Bhuiyan, Mansurul A.; Hasan, Mohammad Al (2016). "PRIIME: A generic framework for interactive personalized interesting pattern discovery". PRIIME: A generic framework for interactive personalized interesting pattern discovery - IEEE Conference Publication. pp. 606–615. arXiv:1607.05749. doi:10.1109/BigData.2016.7840653. ISBN 978-1-4673-9005-7.
  21. ^ "How to get unique data sets on health system". Quora.com. Retrieved 24 May 2018.
  22. ^ "Exploratory Statistical Analysis of EMR data Or Where Angels Fear to tread…". Linkedin.com. 17 October 2015.
  23. ^ "Robot". Acictworld.blogspot.com. 31 December 2015. Retrieved 24 May 2018.
  24. ^ "Obstacle Avoider Robotic Vehicle" (PDF). Repository.sustech.edu. Retrieved 24 May 2018.
  25. ^ Nithya, M.; Sheela, T. (4 January 2018). "Predictive delimiter for multiple sensitive attribute publishing". Cluster Computing: 1–8. doi:10.1007/s10586-017-1612-y.
  26. ^ Janaswamy, Sreya; Kent, Robert D. (2016). "Semantic Interoperability and Data Mapping in EHR Systems". 2016 IEEE 6th International Conference on Advanced Computing (IACC). pp. 117–122. doi:10.1109/IACC.2016.31. ISBN 978-1-4673-8286-1.
  27. ^ "View of Relational Forecast Limiter Algorithm for ICD based EMRs".
  28. ^ "Improving patient screening by applying predictive analytics to electronic medical records.: Big data conference & machine learning training | Strata Data".
  29. ^ https://www.linkedin.com/pulse/part-deux-exploratory-analysis-emr-data-rajeev-gangal/
  30. ^ https://repository.eafit.edu.co/bitstream/handle/10784/13027/ElkinAndrés_VillaSámchez_2018.pdf
  31. ^ http://insticc.org/node/TechnicalProgram/ict4awe/presentationDetails/77986
  32. ^ https://xuc.me/file/paper/ICDE19a.pdf
  33. ^ Chen, J; Chun, D; Patel, M; Chiang, E; James, J (2019). "The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures". BMC Med Inform Decis Mak. 19: 44. doi:10.1186/s12911-019-0793-0. PMC 6416981. PMID 30871520.
  34. ^ Chen, J; Chun, D; Patel, M; Chiang, E; James, J (2019). "The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures". BMC Med Inform Decis Mak. 19: 44. doi:10.1186/s12911-019-0793-0. PMC 6416981. PMID 30871520.
  35. ^ https://www.scribd.com/document/411608802/RobertoCarlosCavalcantieCavalcanteDissertacao2018-EMRBots-org
  36. ^ https://github.com/kartoun/IBM-Harvard-Workshop/
  37. ^ https://h-suwa.github.io/percom2019/papers/p282-li.pdf
  38. ^ 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].
  39. ^ "Contribute to gyaneshanand/Rajasthan_Hackathon_5.0 development by creating an account on GitHub". 2018-07-26.
  40. ^ "HackPrinceton Fall 2018 Workshops". 2018-11-10.
  41. ^ Kartoun, Uri (2018-11-10). "Advancing informatics with electronic medical records bots (HackPrinceton 2018)".
  42. ^ https://hackprinceton.com/hack/web-resources/
  43. ^ https://live.treehacks.com/
  44. ^ http://emrbots.org
  45. ^ "EMRBots: A 100-patient database". 2018-09-03.
  46. ^ "EMRBots: A 10,000-patient database". 2018-09-03.
  47. ^ "EMRBots: A 100,000-patient database". 2018-09-03.
  48. ^ "EMRBots: Full source code". 2018-09-03.
  49. ^ "At Northwell Health, IT Leaders are Revamping the EHR with AI, NLP and Voice Tools".
  50. ^ "Northwell rewards innovative employee projects with funding".
  51. ^ "Northwell rewards innovative employee projects with $500K funding". 2018-05-16.
  52. ^ "Northwell funds innovative employee projects | Northwell Health".
  53. ^ "EMR chatbot takes 2nd place in Northwell Health's 2018 innovation challenge".
  54. ^ "At Northwell Health, IT Leaders are Revamping the EHR with AI, NLP and Voice Tools".
  55. ^ "Advanced test, talking medical records win Northwell funding". 2018-05-16.
  56. ^ https://amp.fox5vegas.com/story/38196671/%7B%7BampLink%7D%7D
  57. ^ "EMRBot Archives –".
  58. ^ "Researchers look for ways to make EHRs easier to use".
  59. ^ "NYC doctors in small practices experience less burnout, study finds". 2018-07-06.
  60. ^ "At Northwell Health, IT Leaders are Revamping the EHR with AI, NLP and Voice Tools".
  61. ^ "CEO da Northwell Health defende aplicação do modelo Shark Tank para estimular inovação em saúde".
  62. ^ https://www.northwell.edu/sites/northwell/files/New-Standard-volume-1-2018_0.pdf
  63. ^ Walonoski, J; et al. (2017). "Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record". J Am Med Inform Assoc. 25 (3): 230–238. doi:10.1093/jamia/ocx079. PMID 29025144.
  64. ^ "Corrigendum". Journal of the American Medical Informatics Association. 25 (7): 921. 2017. doi:10.1093/jamia/ocx147. PMC 6016640. PMID 29253166.
  65. ^ "Realism in Synthetic Data Generation" (PDF). Mro.massey.ac.nz. Retrieved 24 May 2018.