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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.<ref>{{cite web |url= http://www.kdd.org/kdd2017/papers/view/patient-subtyping-via-time-aware-lstm-networks |title= Patient Subtyping via Time-Aware LSTM Networks |website= Kdd.org |accessdate= 24 May 2018}}</ref><ref>{{cite web |url= http://www.kdd.org |title=SIGKDD |website= Kdd.org |accessdate= 24 May 2018}}</ref> 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.<ref>{{cite journal |title=Long short-term memory |journal=Neural Comput.|volume=9 |issue=8 |pages=1735–1780 |year=1997 |last1=Hochreiter | first1 = Sepp | last2=Schmidhuber| first2 = Jürgen }}</ref> 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.<ref>https://epubs.siam.org/doi/abs/10.1137/1.9781611975321.30</ref>
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.<ref>{{cite web |url= http://www.kdd.org/kdd2017/papers/view/patient-subtyping-via-time-aware-lstm-networks |title= Patient Subtyping via Time-Aware LSTM Networks |website= Kdd.org |accessdate= 24 May 2018}}</ref><ref>{{cite web |url= http://www.kdd.org |title=SIGKDD |website= Kdd.org |accessdate= 24 May 2018}}</ref> 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.<ref>{{cite journal |title=Long short-term memory |journal=Neural Comput.|volume=9 |issue=8 |pages=1735–1780 |year=1997 |last1=Hochreiter | first1 = Sepp | last2=Schmidhuber| first2 = Jürgen }}</ref> 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.<ref>https://epubs.siam.org/doi/abs/10.1137/1.9781611975321.30</ref>


Additional use includes [[The University of Chicago]],<ref>{{cite web|url=http://cri.uchicago.edu/wp-content/uploads/2018/02/CRI_StatisticalModeling_Methods.pdf|format=PDF|title=Statistical Modeling of Clinical Data|website=Cri.uchicago.edu|accessdate=24 May 2018}}</ref> [[University of California Merced]],<ref>{{cite web|url=http://ieeexplore.ieee.org/document/7454539/|title=A dynamic cloud computing platform for eHealth systems - IEEE Conference Publication|website=Ieeexplore.ieee.org}}</ref><ref>{{cite web|url=http://cloudlab.ucmerced.edu/mehdi-bahrami-publication|title=Publication - UC Merced Cloud Lab|website=Cloudlab.ucmerced.edu}}</ref> and The [[University of Tampere]], Finland.<ref>{{cite web|url=https://people.uta.fi/~kostas.stefanidis/docs/recsys17/lecture08_fairgrouprecs.pdf|format=PDF|title=Fairness in Group Recommendations in the Health Domain|website=People.uta.fi|accessdate=24 May 2018}}</ref><ref>{{cite web|url=https://devpost.com/software/mlarapp|title=MLARAPP|website=Devpost.com|accessdate=24 May 2018}}</ref> Additional resources include.<ref>{{cite web|url=https://github.com/illidanlab/T-LSTM/blob/master/main.py|title=illidanlab/T-LSTM|website=GitHub|accessdate=24 May 2018}}</ref><ref>{{cite web|url=https://2018.eswc-conferences.org/wp-content/uploads/2018/02/ESWC2018_paper_58.pdf|format=PDF|title=FairGRecs: Fair Group Recommendations by Exploiting Personal Health Information|website=2018.eswc-conferences.org|accessdate=24 May 2018}}</ref><ref>{{cite biorxiv |title= Teaching data science fundamentals through realistic synthetic clinical cardiovascular data |biorxiv=232611}}</ref><ref>{{cite web |url= http://ieeexplore.ieee.org/document/7840653/ |title= PRIIME: A generic framework for interactive personalized interesting pattern discovery - IEEE Conference Publication |website= Ieeexplore.ieee.org |accessdate= 24 May 2018}}</ref><ref>{{cite web |url= http://dmgroup.cs.iupui.edu/files/student_thesis/MansurulBhuiyan_thesis.pdf |format= PDF |title= GENERIC FRAMEWORKS FOR INTERACTIVE PERSONALIZED INTERESTING PATTERN DISCOVERY |website= Dmgroup.cs.iupui.edu |accessdate= 24 May 2018}}</ref><ref>{{cite web |url= https://www.quora.com/How-do-I-get-unique-data-sets-on-health-system |title= How to get unique data sets on health system |website= Quora.com |accessdate= 24 May 2018}}</ref><ref>{{cite web |url= https://www.linkedin.com/pulse/exploratory-statistical-analysis-emr-data-where-angels-rajeev-gangal/|title=Exploratory Statistical Analysis of EMR data Or Where Angels Fear to tread…|date=17 October 2015|website=Linkedin.com}}</ref><ref>{{cite web|url=http://acictworld.blogspot.com/2015/12/robot.html|title=Robot|date=31 December 2015|website=Acictworld.blogspot.com|accessdate=24 May 2018}}</ref><ref>{{cite web|url=http://repository.sustech.edu/bitstream/handle/123456789/15777/Obstacle%20Avoider%20Robotic%20Vehicle.pdf?sequence=1|format=PDF|title=Obstacle Avoider Robotic Vehicle |website=Repository.sustech.edu|accessdate=24 May 2018}}</ref><ref>{{cite journal|url=https://link.springer.com/article/10.1007/s10586-017-1612-y|title=Predictive delimiter for multiple sensitive attribute publishing|first1=M.|last1=Nithya|first2=T.|last2=Sheela|date=4 January 2018|journal=Cluster Computing|pages=1–8|doi=10.1007/s10586-017-1612-y}}</ref><ref>https://ieeexplore.ieee.org/document/7544820/</ref><ref>https://www.sciencepubco.com/index.php/ijet/article/view/18782/8582</ref><ref>https://conferences.oreilly.com/strata/strata-ny/public/schedule/detail/68054</ref><ref>https://www.linkedin.com/pulse/part-deux-exploratory-analysis-emr-data-rajeev-gangal/</ref><ref>https://repository.eafit.edu.co/bitstream/handle/10784/13027/ElkinAndrés_VillaSámchez_2018.pdf</ref>
Additional use includes [[The University of Chicago]],<ref>{{cite web|url=http://cri.uchicago.edu/wp-content/uploads/2018/02/CRI_StatisticalModeling_Methods.pdf|format=PDF|title=Statistical Modeling of Clinical Data|website=Cri.uchicago.edu|accessdate=24 May 2018}}</ref> [[University of California Merced]],<ref>{{cite web|url=http://ieeexplore.ieee.org/document/7454539/|title=A dynamic cloud computing platform for eHealth systems - IEEE Conference Publication|website=Ieeexplore.ieee.org}}</ref><ref>{{cite web|url=http://cloudlab.ucmerced.edu/mehdi-bahrami-publication|title=Publication - UC Merced Cloud Lab|website=Cloudlab.ucmerced.edu}}</ref> and The [[University of Tampere]], Finland.<ref>{{cite web|url=https://people.uta.fi/~kostas.stefanidis/docs/recsys17/lecture08_fairgrouprecs.pdf|format=PDF|title=Fairness in Group Recommendations in the Health Domain|website=People.uta.fi|accessdate=24 May 2018}}</ref><ref>{{cite web|url=https://devpost.com/software/mlarapp|title=MLARAPP|website=Devpost.com|accessdate=24 May 2018}}</ref> Additional resources include.<ref>{{cite web|url=https://github.com/illidanlab/T-LSTM/blob/master/main.py|title=illidanlab/T-LSTM|website=GitHub|accessdate=24 May 2018}}</ref><ref>{{cite web|url=https://link.springer.com/chapter/10.1007/978-3-319-98812-2_11|format=PDF|title=FairGRecs: Fair Group Recommendations by Exploiting Personal Health Information|website=link.springer.com|accessdate=31 October 2018}}</ref><ref>{{cite biorxiv |title= Teaching data science fundamentals through realistic synthetic clinical cardiovascular data |biorxiv=232611}}</ref><ref>{{cite web |url= http://ieeexplore.ieee.org/document/7840653/ |title= PRIIME: A generic framework for interactive personalized interesting pattern discovery - IEEE Conference Publication |website= Ieeexplore.ieee.org |accessdate= 24 May 2018}}</ref><ref>{{cite web |url= http://dmgroup.cs.iupui.edu/files/student_thesis/MansurulBhuiyan_thesis.pdf |format= PDF |title= GENERIC FRAMEWORKS FOR INTERACTIVE PERSONALIZED INTERESTING PATTERN DISCOVERY |website= Dmgroup.cs.iupui.edu |accessdate= 24 May 2018}}</ref><ref>{{cite web |url= https://www.quora.com/How-do-I-get-unique-data-sets-on-health-system |title= How to get unique data sets on health system |website= Quora.com |accessdate= 24 May 2018}}</ref><ref>{{cite web |url= https://www.linkedin.com/pulse/exploratory-statistical-analysis-emr-data-where-angels-rajeev-gangal/|title=Exploratory Statistical Analysis of EMR data Or Where Angels Fear to tread…|date=17 October 2015|website=Linkedin.com}}</ref><ref>{{cite web|url=http://acictworld.blogspot.com/2015/12/robot.html|title=Robot|date=31 December 2015|website=Acictworld.blogspot.com|accessdate=24 May 2018}}</ref><ref>{{cite web|url=http://repository.sustech.edu/bitstream/handle/123456789/15777/Obstacle%20Avoider%20Robotic%20Vehicle.pdf?sequence=1|format=PDF|title=Obstacle Avoider Robotic Vehicle |website=Repository.sustech.edu|accessdate=24 May 2018}}</ref><ref>{{cite journal|url=https://link.springer.com/article/10.1007/s10586-017-1612-y|title=Predictive delimiter for multiple sensitive attribute publishing|first1=M.|last1=Nithya|first2=T.|last2=Sheela|date=4 January 2018|journal=Cluster Computing|pages=1–8|doi=10.1007/s10586-017-1612-y}}</ref><ref>https://ieeexplore.ieee.org/document/7544820/</ref><ref>https://www.sciencepubco.com/index.php/ijet/article/view/18782/8582</ref><ref>https://conferences.oreilly.com/strata/strata-ny/public/schedule/detail/68054</ref><ref>https://www.linkedin.com/pulse/part-deux-exploratory-analysis-emr-data-rajeev-gangal/</ref><ref>https://repository.eafit.edu.co/bitstream/handle/10784/13027/ElkinAndrés_VillaSámchez_2018.pdf</ref>


==Use in hackathons==
==Use in hackathons==

Revision as of 02:12, 1 November 2018

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. 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]

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 illness. 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".[3] 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.[4][5] 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.[6] 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.[7]

Additional use includes The University of Chicago,[8] University of California Merced,[9][10] and The University of Tampere, Finland.[11][12] Additional resources include.[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]

Use in hackathons

Researchers from Carnegie Mellon University used EMRBots data at the CMU HackAuton hackathon to create a prediction tool.[28]. Another use is available.[29]

Availability

The repositories can be downloaded after registration.[30]

The repositories are available to download from Figshare without registration.[31][32][33]

Full source code for creating the repositories is available to download from Figshare.[34]

Northwell Health's EMRBot

In May 2018 Northwell Health funded a project denoted as EMRBot in the health system's third annual innovation challenge.[35][36][37][38] [39][40][41][42][43][44][45][46][47][48] 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."[49][50] An additional criticism is described in a thesis ("Realism in Synthetic Data Generation") granted by Massey University.[51]

References

  1. ^ Kartoun, Uri (2016). "A methodology to generate virtual patient repositories". arXiv:1608.00570.
  2. ^ CACM Staff (2018). "A leap from artificial to intelligence". Communications of the ACM. 60 (1): 10–11.
  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. doi:10.1093/bioinformatics/bty315.
  4. ^ "Patient Subtyping via Time-Aware LSTM Networks". Kdd.org. Retrieved 24 May 2018.
  5. ^ "SIGKDD". Kdd.org. Retrieved 24 May 2018.
  6. ^ Hochreiter, Sepp; Schmidhuber, Jürgen (1997). "Long short-term memory". Neural Comput. 9 (8): 1735–1780.
  7. ^ https://epubs.siam.org/doi/abs/10.1137/1.9781611975321.30
  8. ^ "Statistical Modeling of Clinical Data" (PDF). Cri.uchicago.edu. Retrieved 24 May 2018.
  9. ^ "A dynamic cloud computing platform for eHealth systems - IEEE Conference Publication". Ieeexplore.ieee.org.
  10. ^ "Publication - UC Merced Cloud Lab". Cloudlab.ucmerced.edu.
  11. ^ "Fairness in Group Recommendations in the Health Domain" (PDF). People.uta.fi. Retrieved 24 May 2018.
  12. ^ "MLARAPP". Devpost.com. Retrieved 24 May 2018.
  13. ^ "illidanlab/T-LSTM". GitHub. Retrieved 24 May 2018.
  14. ^ "FairGRecs: Fair Group Recommendations by Exploiting Personal Health Information" (PDF). link.springer.com. Retrieved 31 October 2018.
  15. ^ "Teaching data science fundamentals through realistic synthetic clinical cardiovascular data". bioRxiv 232611. {{cite bioRxiv}}: Check |biorxiv= value (help)
  16. ^ "PRIIME: A generic framework for interactive personalized interesting pattern discovery - IEEE Conference Publication". Ieeexplore.ieee.org. Retrieved 24 May 2018.
  17. ^ "GENERIC FRAMEWORKS FOR INTERACTIVE PERSONALIZED INTERESTING PATTERN DISCOVERY" (PDF). Dmgroup.cs.iupui.edu. Retrieved 24 May 2018.
  18. ^ "How to get unique data sets on health system". Quora.com. Retrieved 24 May 2018.
  19. ^ "Exploratory Statistical Analysis of EMR data Or Where Angels Fear to tread…". Linkedin.com. 17 October 2015.
  20. ^ "Robot". Acictworld.blogspot.com. 31 December 2015. Retrieved 24 May 2018.
  21. ^ "Obstacle Avoider Robotic Vehicle" (PDF). Repository.sustech.edu. Retrieved 24 May 2018.
  22. ^ 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.
  23. ^ https://ieeexplore.ieee.org/document/7544820/
  24. ^ https://www.sciencepubco.com/index.php/ijet/article/view/18782/8582
  25. ^ https://conferences.oreilly.com/strata/strata-ny/public/schedule/detail/68054
  26. ^ https://www.linkedin.com/pulse/part-deux-exploratory-analysis-emr-data-rajeev-gangal/
  27. ^ https://repository.eafit.edu.co/bitstream/handle/10784/13027/ElkinAndrés_VillaSámchez_2018.pdf
  28. ^ 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].
  29. ^ https://github.com/gyaneshanand/Rajasthan_Hackathon_5.0/tree/master/corpus
  30. ^ http://emrbots.org
  31. ^ https://figshare.com/articles/A_100-patient_database/7040039
  32. ^ https://figshare.com/articles/A_10_000-patient_database/7040060
  33. ^ https://figshare.com/articles/EMRBots_a_100_000-patient_database/7040198
  34. ^ https://figshare.com/articles/EMRBots_full_source_code/7040204
  35. ^ https://www.healthcare-informatics.com/article/ehr/northwell-health-it-leaders-are-revamping-ehr-ai-nlp-and-voice-tools
  36. ^ https://www.prnewswire.com/news-releases/northwell-rewards-innovative-employee-projects-with-funding-300648947.html
  37. ^ https://theislandnow.com/manhasset-107/northwell-rewards-innovative-employee-projects-with-500k-funding/
  38. ^ https://www.northwell.edu/about/news/press-releases/northwell-funds-innovative-employee-projects
  39. ^ https://www.beckershospitalreview.com/healthcare-information-technology/emr-chatbot-takes-2nd-place-in-northwell-health-s-2018-innovation-challenge.html
  40. ^ https://www.healthcare-informatics.com/article/ehr/northwell-health-it-leaders-are-revamping-ehr-ai-nlp-and-voice-tools
  41. ^ https://libn.com/2018/05/16/advanced-test-talking-medical-records-win-northwell-funding/
  42. ^ https://amp.fox5vegas.com/story/38196671/%7B%7BampLink%7D%7D
  43. ^ https://huntingtonnow.com/tag/emrbot/
  44. ^ http://www.smartbrief.com/branded/94A57BF5-E8A1-4598-B740-5CB55226F136/06E53646-263A-4690-986A-E861ED3A9638
  45. ^ https://www.crainsnewyork.com/article/20180709/PULSE/180709943/nyc-doctors-in-small-practices-experience-less-burnout-study-finds
  46. ^ https://www.bioportfolio.com/news/article/3677635/At-Northwell-Health-IT-Leaders-are-Revamping-the-EHR-with-AI-NLP.html
  47. ^ http://www.diagnosticoweb.com.br/noticias/gestao/ceo-da-northwell-health-defende-aplicacao-do-modelo-shark-tank-para-estimular-inovacao-em-saude.html
  48. ^ https://www.northwell.edu/sites/northwell/files/New-Standard-volume-1-2018_0.pdf
  49. ^ 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. doi:10.1093/jamia/ocx079. PMID 29025144. {{cite journal}}: Explicit use of et al. in: |last1= (help)
  50. ^ "Corrigendum". Journal of the American Medical Informatics Association. 2017. doi:10.1093/jamia/ocx147.
  51. ^ "Realism in Synthetic Data Generation" (PDF). Mro.massey.ac.nz. Retrieved 24 May 2018.
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