请输入您要查询的百科知识：

　

词条 EMRBots
释义

  1. Background

  2. Academic use

  3. Use in hackathons

  4. Availability

  5. Northwell Health's EMRBot

  6. Criticism

  7. References

{{orphan|date=May 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 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".[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][6] 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.[7] 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.[8] Additional use includes The University of Chicago,[9] University of California Merced,[10][11] and The University of Tampere, Finland.[12][13] Additional resources include.[14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]

In March 2019 the repositories were used to enhance "Computationally-Enabled Medicine", a course given by Harvard Medical School.[29] 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[30]

Use in hackathons

Researchers from Carnegie Mellon University used EMRBots data at the CMU HackAuton hackathon to create a prediction tool.[31]. Additional uses are available.[32]

EMRBots were presented at HackPrinceton 2018 organized by Princeton University.[33][34][35]

EMRBots were presented at TreeHacks 2019 organized by Stanford University.[36]

Availability

The repositories can be downloaded after registration.[37]

The repositories are available to download from Figshare without registration.[38][39][40]

Full source code for creating the repositories is available to download from Figshare.[41]

Northwell Health's EMRBot

In May 2018 Northwell Health funded a project denoted as EMRBot in the health system's third annual innovation challenge.[42][43][44][45]

[46][47][48][49][50][51][52][53][54][55] 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."[56][57] An additional criticism is described in a thesis ("Realism in Synthetic Data Generation") granted by Massey University.[58]

References

1. ^{{cite arxiv |title=A methodology to generate virtual patient repositories |year=2016 |last1=Kartoun |first1=Uri |eprint=1608.00570 |class=cs.CY }}
2. ^{{cite journal |title=A leap from artificial to intelligence |journal=Communications of the ACM |volume=60 |issue=1 |pages=10–11 |year=2018 |last1=CACM Staff | url=https://dl.acm.org/citation.cfm?id=3176926.3168260|doi=10.1145/3034429 }}
3. ^{{cite journal|title=comoRbidity: an R package for the systematic analysis of disease comorbidities|first1=Alba|last1=Gutiérrez-Sacristán|first2=Àlex|last2=Bravo|first3=Alexia|last3=Giannoula|first4=Miguel A.|last4=Mayer|first5=Ferran|last5=Sanz|first6=Laura I.|last6=Furlong|journal=Bioinformatics|volume = 34|issue=18|pages=3228–3230|doi=10.1093/bioinformatics/bty315|pmid=29897411|pmc=6137966|year=2018}}
4. ^{{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}}
5. ^{{cite web |url= http://www.kdd.org |title=SIGKDD |website= Kdd.org |accessdate= 24 May 2018}}
6. ^http://biometrics.cse.msu.edu/Presentations/InciBaytas_PatientSubtypingViaTimeAwareLSTMNetworks_KDD_2017.pdf
7. ^{{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}}
8. ^{{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}}
9. ^{{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}}
10. ^{{Cite book|chapter-url=http://ieeexplore.ieee.org/document/7454539/|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}}
11. ^{{cite web|url=http://cloudlab.ucmerced.edu/mehdi-bahrami-publication|title=Publication - UC Merced Cloud Lab|website=Cloudlab.ucmerced.edu}}
12. ^{{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}}
13. ^{{cite web|url=https://devpost.com/software/mlarapp|title=MLARAPP|website=Devpost.com|accessdate=24 May 2018}}
14. ^{{cite web|url=https://github.com/illidanlab/T-LSTM/blob/master/main.py|title=illidanlab/T-LSTM|website=GitHub|accessdate=24 May 2018}}
15. ^{{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|url = http://tampub.uta.fi/handle/10024/104308}}
16. ^{{cite biorxiv |title= Teaching data science fundamentals through realistic synthetic clinical cardiovascular data |biorxiv=232611}}
17. ^{{Cite book |chapter-url= http://ieeexplore.ieee.org/document/7840653/ |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 }}
18. ^{{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}}
19. ^{{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}}
20. ^{{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}}
21. ^{{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}}
22. ^{{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}}
23. ^{{cite journal|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}}
24. ^{{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| url=https://scholar.uwindsor.ca/etd/5645}}
25. ^{{cite web | url=https://www.sciencepubco.com/index.php/ijet/article/view/18782/8582 | title=View of Relational Forecast Limiter Algorithm for ICD based EMRs}}
26. ^{{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 | Strata Data}}
27. ^https://www.linkedin.com/pulse/part-deux-exploratory-analysis-emr-data-rajeev-gangal/
28. ^https://repository.eafit.edu.co/bitstream/handle/10784/13027/ElkinAndrés_VillaSámchez_2018.pdf
29. ^https://github.com/kartoun/IBM-Harvard-Workshop/
30. ^https://h-suwa.github.io/percom2019/papers/p282-li.pdf
31. ^{{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 }}
32. ^{{cite web | url=https://github.com/gyaneshanand/Rajasthan_Hackathon_5.0/tree/master/corpus | title=Contribute to gyaneshanand/Rajasthan_Hackathon_5.0 development by creating an account on GitHub| date=2018-07-26}}
33. ^{{cite web | url=https://hackprinceton.com/hack/workshops/ | title=HackPrinceton Fall 2018 Workshops| date=2018-11-10}}
34. ^{{cite web | 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}}
35. ^https://hackprinceton.com/hack/web-resources/
36. ^https://live.treehacks.com/
37. ^http://emrbots.org
38. ^{{cite web | url=https://figshare.com/articles/A_100-patient_database/7040039 | title=EMRBots: A 100-patient database| date=2018-09-03}}
39. ^{{cite web | url=https://figshare.com/articles/A_10_000-patient_database/7040060 | title=EMRBots: A 10,000-patient database| date=2018-09-03}}
40. ^{{cite web | url=https://figshare.com/articles/EMRBots_a_100_000-patient_database/7040198 | title=EMRBots: A 100,000-patient database| date=2018-09-03}}
41. ^{{cite web | url=https://figshare.com/articles/EMRBots_full_source_code/7040204 | title=EMRBots: Full source code| date=2018-09-03}}
42. ^{{cite web | url=https://www.healthcare-informatics.com/article/ehr/northwell-health-it-leaders-are-revamping-ehr-ai-nlp-and-voice-tools | title=At Northwell Health, IT Leaders are Revamping the EHR with AI, NLP and Voice Tools}}
43. ^{{cite web | url=https://www.prnewswire.com/news-releases/northwell-rewards-innovative-employee-projects-with-funding-300648947.html | title=Northwell rewards innovative employee projects with funding}}
44. ^{{cite web | url=https://theislandnow.com/manhasset-107/northwell-rewards-innovative-employee-projects-with-500k-funding/ | title=Northwell rewards innovative employee projects with $500K funding| date=2018-05-16}}
45. ^{{cite web | url=https://www.northwell.edu/about/news/press-releases/northwell-funds-innovative-employee-projects | title=Northwell funds innovative employee projects | Northwell Health}}
46. ^{{cite web | url=https://www.beckershospitalreview.com/healthcare-information-technology/emr-chatbot-takes-2nd-place-in-northwell-health-s-2018-innovation-challenge.html | title=EMR chatbot takes 2nd place in Northwell Health's 2018 innovation challenge}}
47. ^{{cite web | url=https://www.healthcare-informatics.com/article/ehr/northwell-health-it-leaders-are-revamping-ehr-ai-nlp-and-voice-tools | title=At Northwell Health, IT Leaders are Revamping the EHR with AI, NLP and Voice Tools}}
48. ^{{cite web | url=https://libn.com/2018/05/16/advanced-test-talking-medical-records-win-northwell-funding/ | title=Advanced test, talking medical records win Northwell funding| date=2018-05-16}}
49. ^https://amp.fox5vegas.com/story/38196671/%7B%7BampLink%7D%7D
50. ^{{cite web | url=https://huntingtonnow.com/tag/emrbot/ | title=EMRBot Archives –}}
51. ^{{cite web | url=http://www.smartbrief.com/branded/94A57BF5-E8A1-4598-B740-5CB55226F136/06E53646-263A-4690-986A-E861ED3A9638 | title=Researchers look for ways to make EHRs easier to use}}
52. ^{{cite web | url=https://www.crainsnewyork.com/article/20180709/PULSE/180709943/nyc-doctors-in-small-practices-experience-less-burnout-study-finds | title=NYC doctors in small practices experience less burnout, study finds| date=2018-07-06}}
53. ^{{cite web | url=https://www.bioportfolio.com/news/article/3677635/At-Northwell-Health-IT-Leaders-are-Revamping-the-EHR-with-AI-NLP.html | title=At Northwell Health, IT Leaders are Revamping the EHR with AI, NLP and Voice Tools}}
54. ^{{cite web | url=http://www.diagnosticoweb.com.br/noticias/gestao/ceo-da-northwell-health-defende-aplicacao-do-modelo-shark-tank-para-estimular-inovacao-em-saude.html | title=CEO da Northwell Health defende aplicação do modelo Shark Tank para estimular inovação em saúde}}
55. ^https://www.northwell.edu/sites/northwell/files/New-Standard-volume-1-2018_0.pdf
56. ^{{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=2017 | last1=Walonoski|display-authors=etal| first1 = J | pmid=29025144|doi=10.1093/jamia/ocx079 }}
57. ^{{cite journal|title=Corrigendum|journal=Journal of the American Medical Informatics Association|volume = 25|issue=7|pages=921|doi=10.1093/jamia/ocx147|pmid=29253166|pmc=6016640|year=2017}}
58. ^{{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}}

2 : Electronic health record software|Medical scoring system
随便看

　

开放百科全书收录14589846条英语、德语、日语等多语种百科知识，基本涵盖了大多数领域的百科知识，是一部内容自由、开放的电子版国际百科全书。

　

Copyright © 2023 OENC.NET All Rights Reserved
京ICP备2021023879号 更新时间：2024/9/29 21:25:50