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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.

Academic use

In April 2018 Bioinformatics (journal) published a study that relied on EMRBots data to create a new R package denoted as "comoRbidity".[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 creating a highly-detailed tutorial demonstrating how to use R using the repositories,[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][36][37][38][39][40][41][42]

In March 2019 the repositories were used to enhance "Computationally-Enabled Medicine", a course given by Harvard Medical School.[43] 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.[44]

Use in hackathons

Researchers from Carnegie Mellon University used EMRBots data at the CMU HackAuton hackathon to create a prediction tool.[45] Additional uses are available.[46]

EMRBots were presented at HackPrinceton 2018 organized by Princeton University.[47][48][49]

EMRBots were presented at TreeHacks 2019 organized by Stanford University.[50]

Availability

The repositories can be downloaded after registration.[51]

The repositories are available to download from Figshare without registration.[52][53][54]

Full source code for creating the repositories is available to download from Figshare.[55]

All source code for EMRBots is available in Elsevier's Software Impacts GitHub site.[56][57]

Northwell Health's EMRBot

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."[58][59] An additional criticism is described in a thesis ("Realism in Synthetic Data Generation") granted by Massey University.[60]

Other Synthetic Medical Data Resources

CareCloud

MDClone[61]

SyntheticMass[62]

SynTReN[63]

References

  1. ^ Kartoun, Uri (September 2019). "Advancing informatics with electronic medical records bots (EMRBots)". Software Impacts. 2: 100006. doi:10.1016/j.simpa.2019.100006.
  2. ^ Kartoun, Uri (2016). "A methodology to generate virtual patient repositories". arXiv:1608.00570 [cs.CY].
  3. ^ CACM Staff (1 January 2018). "A leap from artificial to intelligence". Communications of the ACM. 61 (1): 10–11. doi:10.1145/3168260.
  4. ^ Gutiérrez-Sacristán, Alba; Bravo, Àlex; Giannoula, Alexia; Mayer, Miguel A; Sanz, Ferran; Furlong, Laura I; Kelso, Janet (15 September 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.
  5. ^ "Patient Subtyping via Time-Aware LSTM Networks". Kdd.org. Retrieved 24 May 2018.
  6. ^ "SIGKDD". Kdd.org. Retrieved 24 May 2018.
  7. ^ "Patient subtyping" (PDF). biometrics.cse.msu.edu. Retrieved 2020-02-03.
  8. ^ "Thesis" (PDF). biometrics.cse.msu.edu. Retrieved 2020-02-03.
  9. ^ Hochreiter, Sepp; Schmidhuber, Jürgen (1997). "Long short-term memory". Neural Comput. 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735. PMID 9377276.
  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. Vol. 11030. pp. 147–155. doi:10.1007/978-3-319-98812-2_11. hdl:10024/104308. ISBN 978-3-319-98811-5.
  18. ^ "Teaching data science fundamentals through realistic synthetic clinical cardiovascular data". bioRxiv 10.1101/232611.
  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.
  20. ^ "Generic frameworks for interactive personalized interesting pattern discovery" (PDF). Dmgroup.cs.iupui.edu. Retrieved 24 May 2018.
  21. ^ "Exploratory Statistical Analysis of EMR data Or Where Angels Fear to tread…". Linkedin.com. 17 October 2015.
  22. ^ "Robot". Acictworld.blogspot.com. 31 December 2015. Retrieved 24 May 2018.
  23. ^ "Obstacle Avoider Robotic Vehicle" (PDF). Repository.sustech.edu. Retrieved 24 May 2018.
  24. ^ Nithya, M.; Sheela, T. (2019). "Predictive delimiter for multiple sensitive attribute publishing". Cluster Computing. 22: 12297–12304. doi:10.1007/s10586-017-1612-y.
  25. ^ 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.
  26. ^ "Improving patient screening by applying predictive analytics to electronic medical records.: Big data conference & machine learning training | Strata Data".
  27. ^ "Part Deux : Exploratory Analysis of EMR data". www.linkedin.com.
  28. ^ "Technical Program". insticc.org.
  29. ^ "Data" (PDF). xuc.me. Retrieved 2020-02-03.
  30. ^ 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 (1): 44. doi:10.1186/s12911-019-0793-0. PMC 6416981. PMID 30871520.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  31. ^ "RobertoCarlosCavalcantieCavalcanteDissertacao2018 / EMRBots.org | Prontuário Eletrônico | Aprendizado de Máquina". Scribd.
  32. ^ "Paper" (PDF). www.ijitee.org. Retrieved 2020-02-03.
  33. ^ "Info" (PDF). sutir.sut.ac.th:8080. Retrieved 2020-02-03.
  34. ^ "Full issue" (PDF). sigmodrecord.org. Retrieved 2020-02-03.
  35. ^ "Publication" (PDF). uclab.khu.ac.kr. Retrieved 2020-02-03.
  36. ^ "Media" (PDF). api.sunlab.org. Retrieved 2020-02-03.
  37. ^ Ayala Solares, Jose Roberto; Diletta Raimondi, Francesca Elisa; Zhu, Yajie; Rahimian, Fatemeh; Canoy, Dexter; Tran, Jenny; Pinho Gomes, Ana Catarina; Payberah, Amir H.; Zottoli, Mariagrazia; Nazarzadeh, Milad; Conrad, Nathalie; Rahimi, Kazem; Salimi-Khorshidi, Gholamreza (January 1, 2020). "Deep learning for electronic health records: A comparative review of multiple deep neural architectures". Journal of Biomedical Informatics. 101: 103337. doi:10.1016/j.jbi.2019.103337. PMID 31916973.
  38. ^ https://medinform.jmir.org/2020/2/e16492/
  39. ^ Multidimensional Group Recommendations in the Health Domain
  40. ^ 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. ISBN 978-1-7281-4199-2.
  41. ^ https://link.springer.com/article/10.1007/s00607-020-00837-2
  42. ^ https://ieeexplore.ieee.org/document/9289980
  43. ^ "kartoun/IBM-Harvard-Workshop". August 18, 2019 – via GitHub.
  44. ^ "POET: Privacy on the Edge with Bidirectional Data Transformations" (PDF). Retrieved 2020-02-03.
  45. ^ 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].
  46. ^ "GitHub - gyaneshanand/Rajasthan_Hackathon_5.0". 2018-07-26.
  47. ^ "HackPrinceton Fall 2018 Workshops". 2018-11-10.
  48. ^ Kartoun, Uri (2018-11-10). "Advancing informatics with electronic medical records bots (HackPrinceton 2018)".
  49. ^ [1]
  50. ^ "TreeHacks 2020". live.treehacks.com.
  51. ^ "EMRBOTS.ORG". EMRBOTS.ORG.
  52. ^ "EMRBots: A 100-patient database". 2018-09-03.
  53. ^ "EMRBots: A 10,000-patient database". 2018-09-03.
  54. ^ "EMRBots: A 100,000-patient database". 2018-09-03.
  55. ^ "EMRBots: Full source code". 2018-09-03.
  56. ^ "SoftwareImpacts/SIMPAC-2019-8". November 20, 2019 – via GitHub.
  57. ^ "Software Impacts" – via www.journals.elsevier.com.
  58. ^ Walonoski, J; et al. (2018). "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.
  59. ^ "Corrigendum". Journal of the American Medical Informatics Association. 25 (7): 921. 2017. doi:10.1093/jamia/ocx147. PMC 6016640. PMID 29253166.
  60. ^ "Realism in Synthetic Data Generation" (PDF). Mro.massey.ac.nz. Retrieved 24 May 2018.
  61. ^ "Israeli healthcare data engine firm MDClone raises $26 mln". Reuters. August 22, 2019 – via www.reuters.com.
  62. ^ "Data". synthea.mitre.org. Retrieved 2020-02-03.
  63. ^ Van Den Bulcke, Tim; Van Leemput, Koenraad; Naudts, Bart; Van Remortel, Piet; Ma, Hongwu; Verschoren, Alain; De Moor, Bart; Marchal, Kathleen (2006). "SynTReN: A generator of synthetic gene expression data for design and analysis of structure learning algorithms". BMC Bioinformatics. 7: 43. doi:10.1186/1471-2105-7-43. PMC 1373604. PMID 16438721.{{cite journal}}: CS1 maint: unflagged free DOI (link)
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