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==Use in Schools==
==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 [http://ai-assist.org/ AI-Assist].<ref>{{cite web |last1=Das |first1=Ishani |title=Clinical Decision Support Tool (CDST) |url=http://ai-assist.org/ |website=AI Assist |access-date=4 April 2022}}</ref>
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 [http://ai-assist.org/ AI-Assist].<ref>{{cite web |last1=Das |first1=Ishani |title=Clinical Decision Support Tool (CDST) |url=http://ai-assist.org/ |website=AI Assist |access-date=4 April 2022 |archive-date=6 October 2022 |archive-url=https://web.archive.org/web/20221006002553/http://ai-assist.org/ |url-status=live }}</ref>


==Academic Use==
==Academic Use==
In April 2018 [[Bioinformatics (journal)]] published a study that relied on EMRBots data to create a new R package denoted as "comoRbidity".<ref>{{cite journal |last1=Gutiérrez-Sacristán |first1=Alba |last2=Bravo |first2=Àlex |last3=Giannoula |first3=Alexia |last4=Mayer |first4=Miguel A |last5=Sanz |first5=Ferran |last6=Furlong |first6=Laura I |last7=Kelso |first7=Janet |title=comoRbidity: an R package for the systematic analysis of disease comorbidities |journal=Bioinformatics |date=15 September 2018 |volume=34 |issue=18 |pages=3228–3230 |doi=10.1093/bioinformatics/bty315 |pmid=29897411 |pmc=6137966 }}</ref> 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.<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><ref>{{cite web|url=http://biometrics.cse.msu.edu/Presentations/InciBaytas_PatientSubtypingViaTimeAwareLSTMNetworks_KDD_2017.pdf |title=Patient subtyping |publisher=biometrics.cse.msu.edu |date= |accessdate=2020-02-03}}</ref><ref>{{cite web|url=http://biometrics.cse.msu.edu/Publications/Thesis/InciBaytas_ContributionsToMatchineLearningInBiomedicalInformation.pdf |title=Thesis |publisher=biometrics.cse.msu.edu |date= |accessdate=2020-02-03}}</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 |doi=10.1162/neco.1997.9.8.1735|pmid=9377276|s2cid=1915014}}</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>{{Cite book | doi=10.1137/1.9781611975321.30|chapter = Health-ATM: A Deep Architecture for Multifaceted Patient Health Record Representation and Risk Prediction|title = Proceedings of the 2018 SIAM International Conference on Data Mining| pages=261–269|year = 2018|last1 = Ma|first1 = Tengfei| last2=Xiao| first2=Cao| last3=Wang| first3=Fei| isbn=978-1-61197-532-1}}</ref> Additional use includes [[The University of Chicago]] creating a highly detailed tutorial demonstrating how to use R using the repositories,<ref>{{cite web|url=http://cri.uchicago.edu/wp-content/uploads/2018/02/CRI_StatisticalModeling_Methods.pdf|title=Statistical Modeling of Clinical Data|website=Cri.uchicago.edu|accessdate=24 May 2018}}</ref> [[University of California Merced]],<ref>{{Cite book|title=A dynamic cloud computing platform for eHealth systems - IEEE Conference Publication|pages=435–438|doi=10.1109/HealthCom.2015.7454539|chapter=A dynamic cloud computing platform for eHealth systems|year=2015|last1=Bahrami|first1=Mehdi|last2=Singhal|first2=Mukesh|isbn=978-1-4673-8325-7|s2cid=25042895}}</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|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|date=29 October 2017 |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 book|doi=10.1007/978-3-319-98812-2_11|title = Database and Expert Systems Applications|volume = 11030|pages = 147–155|series = Lecture Notes in Computer Science|year = 2018|last1 = Stratigi|first1 = Maria|last2 = Kondylakis|first2 = Haridimos|last3 = Stefanidis|first3 = Kostas|isbn = 978-3-319-98811-5 |hdl=10024/104308 }}</ref><ref>{{cite bioRxiv |title= Teaching data science fundamentals through realistic synthetic clinical cardiovascular data |biorxiv=10.1101/232611}}</ref><ref>{{Cite book |title= PRIIME: A generic framework for interactive personalized interesting pattern discovery - IEEE Conference Publication |pages= 606–615 |doi= 10.1109/BigData.2016.7840653 |chapter= PRIIME: A generic framework for interactive personalized interesting pattern discovery |year= 2016 |last1= Bhuiyan |first1= Mansurul A. |last2= Hasan |first2= Mohammad Al |isbn= 978-1-4673-9005-7 |arxiv= 1607.05749 |s2cid= 8454336 }}</ref><ref>{{cite web |url= http://dmgroup.cs.iupui.edu/files/student_thesis/MansurulBhuiyan_thesis.pdf |title= Generic frameworks for interactive personalized interesting pattern discovery |website= Dmgroup.cs.iupui.edu |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|title=Predictive delimiter for multiple sensitive attribute publishing|first1=M.|last1=Nithya|first2=T.|last2=Sheela|journal=Cluster Computing|volume=22|pages=12297–12304|doi=10.1007/s10586-017-1612-y|year=2019|s2cid=12093722}}</ref><ref>{{Cite book | chapter-url=https://ieeexplore.ieee.org/document/7544820 | doi=10.1109/IACC.2016.31| chapter=Semantic Interoperability and Data Mapping in EHR Systems| title=2016 IEEE 6th International Conference on Advanced Computing (IACC)| pages=117–122| year=2016| last1=Janaswamy| first1=Sreya| last2=Kent| first2=Robert D.| isbn=978-1-4673-8286-1| s2cid=17062479| url=https://scholar.uwindsor.ca/etd/5645}}</ref><ref>{{cite web | url=https://conferences.oreilly.com/strata/strata-ny/public/schedule/detail/68054 | title=Improving patient screening by applying predictive analytics to electronic medical records.: Big data conference & machine learning training &#124; Strata Data}}</ref><ref>{{cite web|url=http://insticc.org/node/TechnicalProgram/ict4awe/presentationDetails/77986|title=Technical Program|website=insticc.org}}</ref><ref>{{cite web|url=https://xuc.me/file/paper/ICDE19a.pdf |title=Data |publisher=xuc.me |date= |accessdate=2020-02-03}}</ref><ref name="auto1">{{cite journal| pmc=6416981 | pmid=30871520 | doi=10.1186/s12911-019-0793-0 | volume=19 | issue=1 | title=The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures | year=2019 | journal=BMC Med Inform Decis Mak | page=44 | last1 = Chen | first1 = J | last2 = Chun | first2 = D | last3 = Patel | first3 = M | last4 = Chiang | first4 = E | last5 = James | first5 = J | doi-access=free }}</ref><ref>{{cite web|url=https://www.ijitee.org/wp-content/uploads/papers/v8i11/J99270881019.pdf |title=Paper |publisher=www.ijitee.org |date= |accessdate=2020-02-03}}</ref><ref>{{cite web|url=http://sutir.sut.ac.th:8080/sutir/bitstream/123456789/7846/2/Fulltext.pdf |title=Info |publisher=sutir.sut.ac.th:8080 |date= |accessdate=2020-02-03}}</ref><ref>{{cite web|url=https://sigmodrecord.org/publications/sigmodRecord/1909/pdfs/full-issue.pdf |title=Full issue |publisher=sigmodrecord.org |date= |accessdate=2020-02-03}}</ref><ref>{{cite web|url=http://uclab.khu.ac.kr/resources/publication/C_404.pdf |title=Publication |publisher=uclab.khu.ac.kr |date= |accessdate=2020-02-03}}</ref><ref>{{cite web|url=http://api.sunlab.org/enwiki/static/media/1fF/ai2/5b6aef0d241ba60001bec1bf.pdf |title=Media |publisher=api.sunlab.org |date= |accessdate=2020-02-03}}</ref><ref>{{Cite journal|title=Deep learning for electronic health records: A comparative review of multiple deep neural architectures|first1=Jose Roberto|last1=Ayala Solares|first2=Francesca Elisa|last2=Diletta Raimondi|first3=Yajie|last3=Zhu|first4=Fatemeh|last4=Rahimian|first5=Dexter|last5=Canoy|first6=Jenny|last6=Tran|first7=Ana Catarina|last7=Pinho Gomes|first8=Amir H.|last8=Payberah|first9=Mariagrazia|last9=Zottoli|first10=Milad|last10=Nazarzadeh|first11=Nathalie|last11=Conrad|first12=Kazem|last12=Rahimi|first13=Gholamreza|last13=Salimi-Khorshidi|date=January 1, 2020|journal=Journal of Biomedical Informatics|volume=101|page=103337|doi=10.1016/j.jbi.2019.103337|pmid=31916973|doi-access=free}}</ref><ref>{{Cite journal|url=https://medinform.jmir.org/2020/2/e16492/|doi = 10.2196/16492|title = Analyzing Medical Research Results Based on Synthetic Data and Their Relation to Real Data Results: Systematic Comparison from Five Observational Studies|year = 2020|last1 = Reiner Benaim|first1 = Anat|last2 = Almog|first2 = Ronit|last3 = Gorelik|first3 = Yuri|last4 = Hochberg|first4 = Irit|last5 = Nassar|first5 = Laila|last6 = Mashiach|first6 = Tanya|last7 = Khamaisi|first7 = Mogher|last8 = Lurie|first8 = Yael|last9 = Azzam|first9 = Zaher S.|last10 = Khoury|first10 = Johad|last11 = Kurnik|first11 = Daniel|last12 = Beyar|first12 = Rafael|journal = JMIR Medical Informatics|volume = 8|issue = 2|pages = e16492|pmid = 32130148|pmc = 7059086 | doi-access=free }}</ref><ref>Multidimensional Group Recommendations in the Health Domain</ref><ref>{{Cite book |doi = 10.1109/ICOIN48656.2020.9016461|chapter = Semantic Bridge for Resolving Healthcare Data Interoperability|title = 2020 International Conference on Information Networking (ICOIN)|year = 2020|last1 = Satti|first1 = Fahad Ahmed|last2 = Ali Khan|first2 = Wajahat|last3 = Ali|first3 = Taqdir|last4 = Hussain|first4 = Jamil|last5 = Yu|first5 = Hyeong Won|last6 = Kim|first6 = Seoungae|last7 = Lee|first7 = Sungyoung|pages = 86–91|isbn = 978-1-7281-4199-2|s2cid = 212634693}}</ref><ref>{{Cite journal|doi=10.1007/s00607-020-00837-2|title=Ubiquitous Health Profile (UHPr): A big data curation platform for supporting health data interoperability|year=2020|last1=Satti|first1=Fahad Ahmed|last2=Ali|first2=Taqdir|last3=Hussain|first3=Jamil|last4=Khan|first4=Wajahat Ali|last5=Khattak|first5=Asad Masood|last6=Lee|first6=Sungyoung|journal=Computing|volume=102|issue=11|pages=2409–2444|doi-access=free}}</ref><ref>{{Cite book|chapter-url=https://ieeexplore.ieee.org/document/9289980|doi = 10.1002/9781119644316.ch6|chapter = Information Retrieval from Electronic Health Records|title = Engineering and Technology for Healthcare|year = 2021|last1 = Al-Qahtani|first1 = Meshal|last2 = Katsigiannis|first2 = Stamos|last3 = Ramzan|first3 = Naeem|pages = 117–127|isbn = 9781119644248|s2cid = 229413648}}</ref><ref>http://www.ejournal.org.cn/Jweb_cje/EN/Y2021/V30/I2/219</ref><ref>{{Cite journal|doi = 10.1109/ACCESS.2021.3100686|s2cid = 236940396|title = Unsupervised Semantic Mapping for Healthcare Data Storage Schema|year = 2021|last1 = Satti|first1 = Fahad Ahmed|last2 = Hussain|first2 = Musarrat|last3 = Hussain|first3 = Jamil|last4 = Ali|first4 = Syed Imran|last5 = Ali|first5 = Taqdir|last6 = Bilal|first6 = Hafiz Syed Muhammad|last7 = Chung|first7 = Taechoong|last8 = Lee|first8 = Sungyoung|journal = IEEE Access|volume = 9|pages = 107267–107278|doi-access = free| bibcode=2021IEEEA...9j7267S }}</ref><ref>{{Cite journal|url=https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.6487|doi = 10.1002/cpe.6487|title = A clustering-based anonymization approach for privacy-preserving in the healthcare cloud|year = 2021|last1 = Abbasi|first1 = Afsoon|last2 = Mohammadi|first2 = Behnaz|journal = Concurrency and Computation: Practice and Experience|volume = 34|s2cid = 237767088}}</ref><ref>{{cite web |title=Fast Healthcare Interoperability Resources |url=https://unitn-kdi-2021.github.io/unitn-kdi-2021-website/material/templates/Project_example.pdf |access-date=4 July 2024}}</ref><ref>{{Cite journal | pmid=35421184 | year=2022 | last1=Tan | first1=T. L. | last2=Salam | first2=I. | last3=Singh | first3=M. | title=Blockchain-based healthcare management system with two-side verifiability | journal=PLOS ONE | volume=17 | issue=4 | pages=e0266916 | doi=10.1371/journal.pone.0266916 | pmc=9009638 | bibcode=2022PLoSO..1766916T | doi-access=free }}</ref><ref>{{cite journal | doi=10.1007/s10586-022-03697-x | title=Artificial intelligence based health indicator extraction and disease symptoms identification using medical hypothesis models | journal=Cluster Computing | date=23 August 2022 | last1=Sathish Kumar | first1=L. | last2=Routray | first2=Sidheswar | last3=Prabu | first3=A. V. | last4=Rajasoundaran | first4=S. | last5=Pandimurugan | first5=V. | last6=Mukherjee | first6=Amrit | last7=Al-Numay | first7=Mohammed S. | volume=26 | issue=4 | pages=2325–2337 | pmid=36034677 | pmc=9396605 }}</ref><ref>https://www.gsi.upm.es/en/investigacion?view=publication&task=show&id=648</ref>
In April 2018 [[Bioinformatics (journal)]] published a study that relied on EMRBots data to create a new R package denoted as "comoRbidity".<ref>{{cite journal |last1=Gutiérrez-Sacristán |first1=Alba |last2=Bravo |first2=Àlex |last3=Giannoula |first3=Alexia |last4=Mayer |first4=Miguel A |last5=Sanz |first5=Ferran |last6=Furlong |first6=Laura I |last7=Kelso |first7=Janet |title=comoRbidity: an R package for the systematic analysis of disease comorbidities |journal=Bioinformatics |date=15 September 2018 |volume=34 |issue=18 |pages=3228–3230 |doi=10.1093/bioinformatics/bty315 |pmid=29897411 |pmc=6137966 }}</ref> 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.<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 |archive-date= 26 May 2018 |archive-url= https://web.archive.org/web/20180526194147/http://www.kdd.org/kdd2017/papers/view/patient-subtyping-via-time-aware-lstm-networks |url-status= live }}</ref><ref>{{cite web |url= http://www.kdd.org/ |title= SIGKDD |website= Kdd.org |accessdate= 24 May 2018 |archive-date= 26 May 2018 |archive-url= https://web.archive.org/web/20180526031630/http://www.kdd.org/ |url-status= live }}</ref><ref>{{cite web |url=http://biometrics.cse.msu.edu/Presentations/InciBaytas_PatientSubtypingViaTimeAwareLSTMNetworks_KDD_2017.pdf |title=Patient subtyping |publisher=biometrics.cse.msu.edu |date= |accessdate=2020-02-03 |archive-date=2024-07-06 |archive-url=https://web.archive.org/web/20240706163728/http://biometrics.cse.msu.edu/Presentations/InciBaytas_PatientSubtypingViaTimeAwareLSTMNetworks_KDD_2017.pdf |url-status=live }}</ref><ref>{{cite web |url=http://biometrics.cse.msu.edu/Publications/Thesis/InciBaytas_ContributionsToMatchineLearningInBiomedicalInformation.pdf |title=Thesis |publisher=biometrics.cse.msu.edu |date= |accessdate=2020-02-03 |archive-date=2020-02-10 |archive-url=https://web.archive.org/web/20200210152628/http://biometrics.cse.msu.edu/Publications/Thesis/InciBaytas_ContributionsToMatchineLearningInBiomedicalInformation.pdf |url-status=live }}</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 |doi=10.1162/neco.1997.9.8.1735|pmid=9377276|s2cid=1915014}}</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>{{Cite book | doi=10.1137/1.9781611975321.30|chapter = Health-ATM: A Deep Architecture for Multifaceted Patient Health Record Representation and Risk Prediction|title = Proceedings of the 2018 SIAM International Conference on Data Mining| pages=261–269|year = 2018|last1 = Ma|first1 = Tengfei| last2=Xiao| first2=Cao| last3=Wang| first3=Fei| isbn=978-1-61197-532-1}}</ref> Additional use includes [[The University of Chicago]] creating a highly detailed tutorial demonstrating how to use R using the repositories,<ref>{{cite web|url=http://cri.uchicago.edu/wp-content/uploads/2018/02/CRI_StatisticalModeling_Methods.pdf|title=Statistical Modeling of Clinical Data|website=Cri.uchicago.edu|accessdate=24 May 2018|archive-date=11 March 2018|archive-url=https://web.archive.org/web/20180311141033/http://cri.uchicago.edu/wp-content/uploads/2018/02/CRI_StatisticalModeling_Methods.pdf|url-status=live}}</ref> [[University of California Merced]],<ref>{{Cite book|title=A dynamic cloud computing platform for eHealth systems - IEEE Conference Publication|pages=435–438|doi=10.1109/HealthCom.2015.7454539|chapter=A dynamic cloud computing platform for eHealth systems|year=2015|last1=Bahrami|first1=Mehdi|last2=Singhal|first2=Mukesh|isbn=978-1-4673-8325-7|s2cid=25042895}}</ref><ref>{{cite web|url=http://cloudlab.ucmerced.edu/mehdi-bahrami-publication|title=Publication - UC Merced Cloud Lab|website=Cloudlab.ucmerced.edu|access-date=2018-03-17|archive-date=2018-03-18|archive-url=https://web.archive.org/web/20180318055056/http://cloudlab.ucmerced.edu/mehdi-bahrami-publication|url-status=live}}</ref> and The [[University of Tampere]], Finland.<ref>{{cite web|url=https://people.uta.fi/~kostas.stefanidis/docs/recsys17/lecture08_fairgrouprecs.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|date=29 October 2017|accessdate=24 May 2018|archive-date=18 July 2018|archive-url=https://web.archive.org/web/20180718084241/https://devpost.com/software/mlarapp|url-status=live}}</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|archive-date=9 September 2023|archive-url=https://web.archive.org/web/20230909153742/https://github.com/illidanlab/T-LSTM/blob/master/main.py|url-status=live}}</ref><ref>{{Cite book|doi=10.1007/978-3-319-98812-2_11|title = Database and Expert Systems Applications|volume = 11030|pages = 147–155|series = Lecture Notes in Computer Science|year = 2018|last1 = Stratigi|first1 = Maria|last2 = Kondylakis|first2 = Haridimos|last3 = Stefanidis|first3 = Kostas|isbn = 978-3-319-98811-5 |hdl=10024/104308 }}</ref><ref>{{cite bioRxiv |title= Teaching data science fundamentals through realistic synthetic clinical cardiovascular data |biorxiv=10.1101/232611}}</ref><ref>{{Cite book |title= PRIIME: A generic framework for interactive personalized interesting pattern discovery - IEEE Conference Publication |pages= 606–615 |doi= 10.1109/BigData.2016.7840653 |chapter= PRIIME: A generic framework for interactive personalized interesting pattern discovery |year= 2016 |last1= Bhuiyan |first1= Mansurul A. |last2= Hasan |first2= Mohammad Al |isbn= 978-1-4673-9005-7 |arxiv= 1607.05749 |s2cid= 8454336 }}</ref><ref>{{cite web |url= http://dmgroup.cs.iupui.edu/files/student_thesis/MansurulBhuiyan_thesis.pdf |title= Generic frameworks for interactive personalized interesting pattern discovery |website= Dmgroup.cs.iupui.edu |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|archive-date=13 March 2018|archive-url=https://web.archive.org/web/20180313093139/http://repository.sustech.edu/bitstream/handle/123456789/15777/Obstacle%20Avoider%20Robotic%20Vehicle.pdf?sequence=1|url-status=live}}</ref><ref>{{cite journal|title=Predictive delimiter for multiple sensitive attribute publishing|first1=M.|last1=Nithya|first2=T.|last2=Sheela|journal=Cluster Computing|volume=22|pages=12297–12304|doi=10.1007/s10586-017-1612-y|year=2019|s2cid=12093722}}</ref><ref>{{Cite book| chapter-url=https://ieeexplore.ieee.org/document/7544820| doi=10.1109/IACC.2016.31| chapter=Semantic Interoperability and Data Mapping in EHR Systems| title=2016 IEEE 6th International Conference on Advanced Computing (IACC)| pages=117–122| year=2016| last1=Janaswamy| first1=Sreya| last2=Kent| first2=Robert D.| isbn=978-1-4673-8286-1| s2cid=17062479| url=https://scholar.uwindsor.ca/etd/5645| access-date=2018-11-11| archive-date=2018-11-12| archive-url=https://web.archive.org/web/20181112141453/https://scholar.uwindsor.ca/etd/5645/| url-status=live}}</ref><ref>{{cite web | url=https://conferences.oreilly.com/strata/strata-ny/public/schedule/detail/68054 | title=Improving patient screening by applying predictive analytics to electronic medical records.: Big data conference & machine learning training &#124; Strata Data | access-date=2018-09-19 | archive-date=2018-09-19 | archive-url=https://web.archive.org/web/20180919211548/https://conferences.oreilly.com/strata/strata-ny/public/schedule/detail/68054 | url-status=live }}</ref><ref>{{cite web|url=http://insticc.org/node/TechnicalProgram/ict4awe/presentationDetails/77986|title=Technical 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|archive-url=https://web.archive.org/web/20191215155756/https://sigmodrecord.org/publications/sigmodRecord/1909/pdfs/full-issue.pdf |url-status=live }}</ref><ref>{{cite web |url=http://uclab.khu.ac.kr/resources/publication/C_404.pdf |title=Publication |publisher=uclab.khu.ac.kr |date= |accessdate=2020-02-03 |archive-date=2020-01-14 |archive-url=https://web.archive.org/web/20200114191019/http://uclab.khu.ac.kr/resources/publication/C_404.pdf |url-status=live }}</ref><ref>{{cite web |url=http://api.sunlab.org/enwiki/static/media/1fF/ai2/5b6aef0d241ba60001bec1bf.pdf |title=Media |publisher=api.sunlab.org |date= |accessdate=2020-02-03 |archive-date=2020-02-02 |archive-url=https://web.archive.org/web/20200202183839/http://api.sunlab.org/enwiki/static/media/1fF/ai2/5b6aef0d241ba60001bec1bf.pdf |url-status=live }}</ref><ref>{{Cite journal|title=Deep learning for electronic health records: A comparative review of multiple deep neural architectures|first1=Jose Roberto|last1=Ayala 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= Khamaisi|first7 = Mogher|last8 = Lurie|first8 = Yael|last9 = Azzam|first9 = Zaher S.|last10 = Khoury|first10 = Johad|last11 = Kurnik|first11 = Daniel|last12 = Beyar|first12 = Rafael|journal = JMIR Medical Informatics|volume = 8|issue = 2|pages = e16492|pmid = 32130148|pmc = 7059086|doi-access = free|access-date = 2020-02-21|archive-date = 2020-02-21|archive-url = https://web.archive.org/web/20200221185821/https://medinform.jmir.org/2020/2/e16492/|url-status = live}}</ref><ref>Multidimensional Group Recommendations in the Health Domain</ref><ref>{{Cite book |doi = 10.1109/ICOIN48656.2020.9016461|chapter = Semantic Bridge for Resolving Healthcare Data Interoperability|title = 2020 International Conference on Information Networking (ICOIN)|year = 2020|last1 = Satti|first1 = Fahad Ahmed|last2 = Ali Khan|first2 = Wajahat|last3 = Ali|first3 = Taqdir|last4 = Hussain|first4 = Jamil|last5 = Yu|first5 = Hyeong Won|last6 = Kim|first6 = Seoungae|last7 = Lee|first7 = Sungyoung|pages = 86–91|isbn 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In March 2019 the repositories were used to enhance "Computationally-Enabled Medicine", a course given by Harvard Medical School.<ref>{{cite web|url=https://github.com/kartoun/IBM-Harvard-Workshop|title=kartoun/IBM-Harvard-Workshop|date=August 18, 2019|via=GitHub}}</ref> 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.<ref>{{cite web|url=https://h-suwa.github.io/percom2019/papers/p282-li.pdf |title=POET: Privacy on the Edge with Bidirectional Data Transformations |date= |accessdate=2020-02-03}}</ref>
In March 2019 the repositories were used to enhance "Computationally-Enabled Medicine", a course given by Harvard Medical School.<ref>{{cite web|url=https://github.com/kartoun/IBM-Harvard-Workshop|title=kartoun/IBM-Harvard-Workshop|date=August 18, 2019|via=GitHub|access-date=March 14, 2019|archive-date=October 11, 2020|archive-url=https://web.archive.org/web/20201011014101/https://github.com/kartoun/IBM-Harvard-Workshop|url-status=live}}</ref> 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.<ref>{{cite web |url=https://h-suwa.github.io/percom2019/papers/p282-li.pdf |title=POET: Privacy on the Edge with Bidirectional Data Transformations |date= |accessdate=2020-02-03 |archive-date=2019-04-05 |archive-url=https://web.archive.org/web/20190405224019/https://h-suwa.github.io/percom2019/papers/p282-li.pdf |url-status=live }}</ref>


==Use in Hackathons==
==Use in Hackathons==
Researchers from [[Carnegie Mellon University]] used EMRBots data at the CMU HackAuton [[hackathon]] to create a prediction tool.<ref>{{cite arXiv |title=Characterizing Allegheny County opioid overdoses with an interactive data explorer and synthetic prediction tool |eprint= 1804.08830 |year=2018 |last1=Gebert |first1= Theresa |last2=Jiang |first2= Shuli |last3=Sheng |first3= Jiaxian |class=stat.AP }}</ref> Additional uses are available.<ref>{{cite web | url=https://github.com/gyaneshanand/Rajasthan_Hackathon_5.0/tree/master/corpus | title=GitHub - gyaneshanand/Rajasthan_Hackathon_5.0| website=[[GitHub]]| date=2018-07-26}}</ref>
Researchers from [[Carnegie Mellon University]] used EMRBots data at the CMU HackAuton [[hackathon]] to create a prediction tool.<ref>{{cite arXiv |title=Characterizing Allegheny County opioid overdoses with an interactive data explorer and synthetic prediction tool |eprint= 1804.08830 |year=2018 |last1=Gebert |first1= Theresa |last2=Jiang |first2= Shuli |last3=Sheng |first3= Jiaxian |class=stat.AP }}</ref> Additional uses are available.<ref>{{cite web| url=https://github.com/gyaneshanand/Rajasthan_Hackathon_5.0/tree/master/corpus| title=GitHub - gyaneshanand/Rajasthan_Hackathon_5.0| website=[[GitHub]]| date=2018-07-26| access-date=2018-09-11| archive-date=2024-07-06| archive-url=https://web.archive.org/web/20240706163750/https://github.com/gyaneshanand/Rajasthan_Hackathon_5.0/tree/master/corpus| url-status=live}}</ref>


EMRBots were presented at [[HackPrinceton]] 2018 organized by [[Princeton University]].<ref>{{cite web | url=https://hackprinceton.com/hack/workshops/ | title=HackPrinceton Fall 2018 Workshops| date=2018-11-10}}</ref><ref>{{cite journal | url=https://figshare.com/articles/ADVANCING_INFORMATICS_WITH_ELECTRONIC_MEDICAL_RECORDS_BOTS/7325903 | title=Advancing informatics with electronic medical records bots (HackPrinceton 2018)| date=2018-11-10|last1=Kartoun|first1= Uri| doi=10.6084/m9.figshare.7325903.v1}}</ref><ref>{{cite web |url=https://hackprinceton.com/hack/web-resources/ |title=Web Resources |website=hackprinceton.com |access-date=17 January 2022 |archive-url=https://web.archive.org/web/20181217212446/https://hackprinceton.com/hack/web-resources/ |archive-date=17 December 2018 |url-status=dead}}</ref>
EMRBots were presented at [[HackPrinceton]] 2018 organized by [[Princeton University]].<ref>{{cite web| url=https://hackprinceton.com/hack/workshops/| title=HackPrinceton Fall 2018 Workshops| date=2018-11-10| access-date=2018-11-06| archive-date=2018-11-07| archive-url=https://web.archive.org/web/20181107054244/https://hackprinceton.com/hack/workshops/| url-status=live}}</ref><ref>{{cite journal| url=https://figshare.com/articles/ADVANCING_INFORMATICS_WITH_ELECTRONIC_MEDICAL_RECORDS_BOTS/7325903| title=Advancing informatics with electronic medical records bots (HackPrinceton 2018)| date=2018-11-10| last1=Kartoun| first1=Uri| doi=10.6084/m9.figshare.7325903.v1| access-date=2018-11-12| archive-date=2018-11-13| archive-url=https://web.archive.org/web/20181113075422/https://figshare.com/articles/ADVANCING_INFORMATICS_WITH_ELECTRONIC_MEDICAL_RECORDS_BOTS/7325903| url-status=live}}</ref><ref>{{cite web |url=https://hackprinceton.com/hack/web-resources/ |title=Web Resources |website=hackprinceton.com |access-date=17 January 2022 |archive-url=https://web.archive.org/web/20181217212446/https://hackprinceton.com/hack/web-resources/ |archive-date=17 December 2018 |url-status=dead}}</ref>


EMRBots were presented at TreeHacks 2019 organized by [[Stanford University]].<ref>{{cite web|url=https://live.treehacks.com/|title=TreeHacks 2020|website=live.treehacks.com}}</ref>
EMRBots were presented at TreeHacks 2019 organized by [[Stanford University]].<ref>{{cite web|url=https://live.treehacks.com/|title=TreeHacks 2020|website=live.treehacks.com|access-date=2019-02-20|archive-date=2024-07-06|archive-url=https://web.archive.org/web/20240706164243/https://live.treehacks.com/|url-status=live}}</ref>


==Availability==
==Availability==
The repositories can be downloaded after registration.<ref>{{cite web|url=http://www.emrbots.org/|title=EMRBOTS.ORG|website=EMRBOTS.ORG}}</ref>
The repositories can be downloaded after registration.<ref>{{cite web|url=http://www.emrbots.org/|title=EMRBOTS.ORG|website=EMRBOTS.ORG|access-date=2020-02-03|archive-date=2020-02-03|archive-url=https://web.archive.org/web/20200203175018/http://www.emrbots.org/|url-status=live}}</ref>


The repositories are available to download from [[Figshare]] without registration.<ref>{{cite journal | url=https://figshare.com/articles/A_100-patient_database/7040039 | title=EMRBots: A 100-patient database| date=2018-09-03| doi=10.6084/m9.figshare.7040039.v3| last1=Kartoun| first1=Uri| publisher=figshare}}</ref><ref>{{cite journal | url=https://figshare.com/articles/A_10_000-patient_database/7040060 | title=EMRBots: A 10,000-patient database| date=2018-09-03| doi=10.6084/m9.figshare.7040060.v3| last1=Kartoun| first1=Uri| publisher=figshare}}</ref><ref>{{cite journal | url=https://figshare.com/articles/EMRBots_a_100_000-patient_database/7040198 | title=EMRBots: A 100,000-patient database| date=2018-09-03| doi=10.6084/m9.figshare.7040198.v1| last1=Kartoun| first1=Uri| publisher=figshare}}</ref>
The repositories are available to download from [[Figshare]] without registration.<ref>{{cite journal| url=https://figshare.com/articles/A_100-patient_database/7040039| title=EMRBots: A 100-patient database| date=2018-09-03| doi=10.6084/m9.figshare.7040039.v3| last1=Kartoun| first1=Uri| publisher=figshare| access-date=2018-09-24| archive-date=2018-09-25| archive-url=https://web.archive.org/web/20180925025441/https://figshare.com/articles/A_100-patient_database/7040039| url-status=live}}</ref><ref>{{cite journal| url=https://figshare.com/articles/A_10_000-patient_database/7040060| title=EMRBots: A 10,000-patient database| date=2018-09-03| doi=10.6084/m9.figshare.7040060.v3| last1=Kartoun| first1=Uri| publisher=figshare| access-date=2018-09-24| archive-date=2018-09-24| archive-url=https://web.archive.org/web/20180924224924/https://figshare.com/articles/A_10_000-patient_database/7040060| url-status=live}}</ref><ref>{{cite journal| url=https://figshare.com/articles/EMRBots_a_100_000-patient_database/7040198| title=EMRBots: A 100,000-patient database| date=2018-09-03| doi=10.6084/m9.figshare.7040198.v1| last1=Kartoun| first1=Uri| publisher=figshare| access-date=2018-09-24| archive-date=2018-09-25| archive-url=https://web.archive.org/web/20180925025459/https://figshare.com/articles/EMRBots_a_100_000-patient_database/7040198| url-status=live}}</ref>


Full source code for creating the repositories is available to download from [[Figshare]].<ref>{{cite journal | url=https://figshare.com/articles/EMRBots_full_source_code/7040204 | title=EMRBots: Full source code| date=2018-09-03| doi=10.6084/m9.figshare.7040204.v2| last1=Kartoun| first1=Uri}}</ref>
Full source code for creating the repositories is available to download from [[Figshare]].<ref>{{cite journal| url=https://figshare.com/articles/EMRBots_full_source_code/7040204| title=EMRBots: Full source code| date=2018-09-03| doi=10.6084/m9.figshare.7040204.v2| last1=Kartoun| first1=Uri| access-date=2018-09-24| archive-date=2018-09-25| archive-url=https://web.archive.org/web/20180925025443/https://figshare.com/articles/EMRBots_full_source_code/7040204| url-status=live}}</ref>


All source code for EMRBots is available in [[Elsevier]]'s [[Software Impacts]] [[GitHub]] site.<ref>{{cite web|url=https://github.com/SoftwareImpacts/SIMPAC-2019-8|title=SoftwareImpacts/SIMPAC-2019-8|date=November 20, 2019|via=GitHub}}</ref><ref>{{cite web|url=https://www.journals.elsevier.com/software-impacts/|title=Software Impacts|via=www.journals.elsevier.com}}</ref>
All source code for EMRBots is available in [[Elsevier]]'s [[Software Impacts]] [[GitHub]] site.<ref>{{cite web|url=https://github.com/SoftwareImpacts/SIMPAC-2019-8|title=SoftwareImpacts/SIMPAC-2019-8|date=November 20, 2019|via=GitHub|access-date=September 9, 2019|archive-date=November 24, 2020|archive-url=https://web.archive.org/web/20201124190415/https://github.com/SoftwareImpacts/SIMPAC-2019-8|url-status=live}}</ref><ref>{{cite web|url=https://www.journals.elsevier.com/software-impacts/|title=Software Impacts|via=www.journals.elsevier.com|access-date=2019-09-09|archive-date=2019-09-30|archive-url=https://web.archive.org/web/20190930153846/https://www.journals.elsevier.com/software-impacts/|url-status=live}}</ref>


==Northwell Health's EMRBot==
==Northwell Health's EMRBot==
Line 42: Line 42:


==Criticism==
==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."<ref>{{cite journal |title=Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record |journal=J Am Med Inform Assoc |volume=25 |issue=3 |pages=230–238 |year=2018 | last1=Walonoski|display-authors=etal| first1 = J | pmid=29025144|doi=10.1093/jamia/ocx079 |pmc=7651916 |doi-access=free }}</ref><ref>{{cite journal|title=Corrigendum|journal=Journal of the American Medical Informatics Association|volume = 25|issue=7|page=921|doi=10.1093/jamia/ocx147|pmid=29253166|pmc=6016640|year=2017}}</ref> An additional criticism is described in a thesis ("Realism in Synthetic Data Generation") granted by [[Massey University]].<ref>{{cite web|url=https://mro.massey.ac.nz/bitstream/handle/10179/11569/02_whole.pdf|title=Realism in Synthetic Data Generation|website=Mro.massey.ac.nz|accessdate=24 May 2018}}</ref>
"[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."<ref>{{cite journal |title=Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record |journal=J Am Med Inform Assoc |volume=25 |issue=3 |pages=230–238 |year=2018 | last1=Walonoski|display-authors=etal| first1 = J | pmid=29025144|doi=10.1093/jamia/ocx079 |pmc=7651916 |doi-access=free }}</ref><ref>{{cite journal|title=Corrigendum|journal=Journal of the American Medical Informatics Association|volume = 25|issue=7|page=921|doi=10.1093/jamia/ocx147|pmid=29253166|pmc=6016640|year=2017}}</ref> An additional criticism is described in a thesis ("Realism in Synthetic Data Generation") granted by [[Massey University]].<ref>{{cite web|url=https://mro.massey.ac.nz/bitstream/handle/10179/11569/02_whole.pdf|title=Realism in Synthetic Data Generation|website=Mro.massey.ac.nz|accessdate=24 May 2018|archive-date=6 July 2024|archive-url=https://web.archive.org/web/20240706164250/https://mro.massey.ac.nz/bitstream/handle/10179/11569/02_whole.pdf|url-status=live}}</ref>


==Other Synthetic Medical Data Resources==
==Other Synthetic Medical Data Resources==
CareCloud
CareCloud


[[MDClone]]<ref>{{cite news|url=https://www.reuters.com/article/tech-mdclone-fundraising-idUSL5N25I25X|title=Israeli healthcare data engine firm MDClone raises $26 mln|newspaper=Reuters|date=August 22, 2019|via=www.reuters.com}}</ref>
[[MDClone]]<ref>{{cite news|url=https://www.reuters.com/article/tech-mdclone-fundraising-idUSL5N25I25X|title=Israeli healthcare data engine firm MDClone raises $26 mln|newspaper=Reuters|date=August 22, 2019|via=www.reuters.com|access-date=February 3, 2020|archive-date=October 9, 2019|archive-url=https://web.archive.org/web/20191009015834/https://www.reuters.com/article/tech-mdclone-fundraising-idUSL5N25I25X|url-status=live}}</ref>


[[SyntheticMass]]<ref>{{cite web|url=https://synthea.mitre.org/ |title=Data |publisher=synthea.mitre.org |accessdate=2020-02-03}}</ref>
[[SyntheticMass]]<ref>{{cite web |url=https://synthea.mitre.org/ |title=Data |publisher=synthea.mitre.org |accessdate=2020-02-03 |archive-date=2019-10-19 |archive-url=https://web.archive.org/web/20191019061306/https://synthea.mitre.org/ |url-status=live }}</ref>


[[SynTReN]]<ref>{{Cite journal | doi=10.1186/1471-2105-7-43| pmid=16438721| pmc=1373604| year=2006| last1=Van Den Bulcke| first1=Tim| last2=Van Leemput| first2=Koenraad| last3=Naudts| first3=Bart| last4=Van Remortel| first4=Piet| last5=Ma| first5=Hongwu| last6=Verschoren| first6=Alain| last7=De Moor| first7=Bart| last8=Marchal| first8=Kathleen| title=SynTReN: A generator of synthetic gene expression data for design and analysis of structure learning algorithms| journal=BMC Bioinformatics| volume=7| pages=43| doi-access=free}}</ref>
[[SynTReN]]<ref>{{Cite journal | doi=10.1186/1471-2105-7-43| pmid=16438721| pmc=1373604| year=2006| last1=Van Den Bulcke| first1=Tim| last2=Van Leemput| first2=Koenraad| last3=Naudts| first3=Bart| last4=Van Remortel| first4=Piet| last5=Ma| first5=Hongwu| last6=Verschoren| first6=Alain| last7=De Moor| first7=Bart| last8=Marchal| first8=Kathleen| title=SynTReN: A generator of synthetic gene expression data for design and analysis of structure learning algorithms| journal=BMC Bioinformatics| volume=7| pages=43| doi-access=free}}</ref>

Latest revision as of 16:43, 6 July 2024


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

[edit]

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

[edit]

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]

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".[5] 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.[6][7][8][9] 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.[10] 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.[11] Additional use includes The University of Chicago creating a highly detailed tutorial demonstrating how to use R using the repositories,[12] University of California Merced,[13][14] and The University of Tampere, Finland.[15][16] Additional resources include.[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][43][44][45][46]

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]

Use in Hackathons

[edit]

Researchers from Carnegie Mellon University used EMRBots data at the CMU HackAuton hackathon to create a prediction tool.[49] Additional uses are available.[50]

EMRBots were presented at HackPrinceton 2018 organized by Princeton University.[51][52][53]

EMRBots were presented at TreeHacks 2019 organized by Stanford University.[54]

Availability

[edit]

The repositories can be downloaded after registration.[55]

The repositories are available to download from Figshare without registration.[56][57][58]

Full source code for creating the repositories is available to download from Figshare.[59]

All source code for EMRBots is available in Elsevier's Software Impacts GitHub site.[60][61]

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. 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

[edit]

"[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]

Other Synthetic Medical Data Resources

[edit]

CareCloud

MDClone[65]

SyntheticMass[66]

SynTReN[67]

References

[edit]
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  5. ^ 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.
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  57. ^ Kartoun, Uri (2018-09-03). "EMRBots: A 10,000-patient database". figshare. doi:10.6084/m9.figshare.7040060.v3. Archived from the original on 2018-09-24. Retrieved 2018-09-24. {{cite journal}}: Cite journal requires |journal= (help)
  58. ^ Kartoun, Uri (2018-09-03). "EMRBots: A 100,000-patient database". figshare. doi:10.6084/m9.figshare.7040198.v1. Archived from the original on 2018-09-25. Retrieved 2018-09-24. {{cite journal}}: Cite journal requires |journal= (help)
  59. ^ Kartoun, Uri (2018-09-03). "EMRBots: Full source code". doi:10.6084/m9.figshare.7040204.v2. Archived from the original on 2018-09-25. Retrieved 2018-09-24. {{cite journal}}: Cite journal requires |journal= (help)
  60. ^ "SoftwareImpacts/SIMPAC-2019-8". November 20, 2019. Archived from the original on November 24, 2020. Retrieved September 9, 2019 – via GitHub.
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  63. ^ "Corrigendum". Journal of the American Medical Informatics Association. 25 (7): 921. 2017. doi:10.1093/jamia/ocx147. PMC 6016640. PMID 29253166.
  64. ^ "Realism in Synthetic Data Generation" (PDF). Mro.massey.ac.nz. Archived (PDF) from the original on 6 July 2024. Retrieved 24 May 2018.
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  67. ^ 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.
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