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Reseach Article

Synchronization of Machine Learning into Electronic Health Records

by Meet N. Gandhi, Eshan Vatsa, Nitin S. Choubey
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 26
Year of Publication: 2019
Authors: Meet N. Gandhi, Eshan Vatsa, Nitin S. Choubey
10.5120/ijca2019919751

Meet N. Gandhi, Eshan Vatsa, Nitin S. Choubey . Synchronization of Machine Learning into Electronic Health Records. International Journal of Computer Applications. 177, 26 ( Dec 2019), 40-47. DOI=10.5120/ijca2019919751

@article{ 10.5120/ijca2019919751,
author = { Meet N. Gandhi, Eshan Vatsa, Nitin S. Choubey },
title = { Synchronization of Machine Learning into Electronic Health Records },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2019 },
volume = { 177 },
number = { 26 },
month = { Dec },
year = { 2019 },
issn = { 0975-8887 },
pages = { 40-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number26/31065-2019919751/ },
doi = { 10.5120/ijca2019919751 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:47:01.620344+05:30
%A Meet N. Gandhi
%A Eshan Vatsa
%A Nitin S. Choubey
%T Synchronization of Machine Learning into Electronic Health Records
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 26
%P 40-47
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The introduction of EHR (Electronic Health Record), in the medical field has been under discussion for a while but due to a very low acceptance rate of this technology by physicians, it has proven to be a risky gamble in the successful implementation of EHR. EHR uses data accumulated on the subject’s health to determine tests required, health analysis and real-time records to help the physician provide more accurate and detailed analysis on the subject. Due to Health Information Technology for Economic and Clinical Health (HITECH) there has been an increase in the amount of data accumulation by EHR. The data has great potential because of the large archive of information across the globe, but due to the random collection of data, it has resulted in the development of an unstructured record which has resulted to difficulty in transactions [1]. Even though there has been a large collection of data around the globe, the major issue has been making use of this data in a logical manner for purposeful implementation. The intention behind this paper is how to proceed with the implementation of machine learning in EHR along with its steps in order to analyze the data [2, 3] so that one can understand the pattern generated by the data provided. There are several machine learning algorithms for the interpretation of this data, but not all data are compactable with all the algorithms, thus in this paper the method of data gathering to applying machine learning algorithms on the data is explained and various ways to perform different steps are also discussed in detail.

References
  1. Ohio-Machado L. 2011. Realizing the full potential of electronic health records: the role of natural language processing. J. Am. Med. Inform. Assoc. 18, 539 (doi:10.1136/amiajnl-2011-000501) [PMCfree article] [PubMed]
  2. BruijnBd, Cherry C, Kiritchenko S, Martin J, Zhu X. 2011. Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010. J. Am. Med. Inform. Assoc. 18, 557–562. (doi:10.1136/amiajnl-2011-000150) [PMC free article] [PubMed]
  3. Opportunities and obstacles for deep learning in biology and  medicine_doi: 10.1098/rsif.2017.0387
  4. HIPAA act is available here:https://www.hhs.gov/sites/default/files/ocr/privacy/hipaa/understanding/coveredentities/De-identification/hhs_deid_guidance.pdf)
  5. Opportunities and challenges in developing risk prediction models with electronic health record data: a systematic reviewJ Am Med Inform Assoc. 2017 Jan; 24(1): 198–208.Published online 2016 May 17. doi:  10.1093/jamia/ocw042
  6. Future of electronic health records: implications for decision support. Rothman B, Leonard JC,Vigoda MM Mt Sinai J Med. 2012 Nov-Dec; 79(6):757-68. [PubMed] [Ref list]
  7. Prediction of coronary heart disease using risk factor categories. Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB Circulation. 1998 May 12; 97(18):1837-47.[PubMed] [Ref list]
  8. Hersh WR, Weiner MG, Embi PJ, et al. Caveats for the use of operational electronic health record data in comparative effectiveness research. Med Care. 2013;51(8 Suppl3):S30–S37. [PMC free article][PubMed]
  9. Knowledge Acquisition for Electronic Health Records on clouddoi.org/10.1016/j.procs.2017.08.031
  10. Automatic de-identification of textual documents in the electronic health record: a review of recent research doi:10.1186/1471-2288-10-70
  11. Gardner J, Xiong L: HIDE: An Integrated System for Health Information De-identification. Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems 2008, 254-9.
  12. Aramaki E, et al: Automatic Deidentification by using Sentence Features and Label       Consistency. i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, Washington, DC 2006.
  13. R. M. B. A. Beckwith, U. J. Balis, and F. Kuo. Development and evaluation of an    open source software tool for deidentification of pathology reports. B
  14. Beckwith BA, et al: Development and evaluation of an open source software tool for deidentification of pathology reports. BMC Med Inform DecisMak 2006, 12.
  15. Morrison FP, et al: Repurposing the clinical record: can an existing natural language processing system de-identify clinical notes? J Am Med Inform Assoc 2009, 16(1):37-9
  16. Friedlin FJ, McDonald CJ: A software tool for removing patient identifying information from clinical documents. J Am Med Inform Assoc 2008, 15(5):601-10.
  17. Hadoop Development Available: https://metadesignsolutions.com/hadoop-development
  18. Thomas SM, et al: A successful technique for removing names in pathology reports using an augmented search and replace method. Proc AMIA Symp 2002, 777-81.
  19. Taira RK, Bui AA, Kangarloo H: Identification of patient name referenceswithin medical documents using semantic selectional restrictions. Proc AMIA Symp 2002, 757-61.
  20. Using your electronic medical record for research: a primer for avoiding pitfalls https://doi.org/10.1093/fampra/cmp068
  21. Uzuner O, et al: A de-identifier for medical discharge summaries. ArtifIntell Med. 2008, 42 (1): 13-35. 10.1016/j.artmed.2007.10.001.
  22. Wimmer H, Powell LM. A comparison of open source tools for sentiment analysis. 2015;1–9. Available:http://fotiad.is/blog/sentiment-analysis-comparison/.
  23. Jovic, A, Brkic K, Bogunovic N. An overview of free software tools for general data mining. Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014 37th International Convention on.IEEE. 2014: 1112–1117.
  24. Herschel G, Linden A, Kart L. Magic quadrant for advanced analytics platforms. Available:http://www.gartner.com/technology/reprints.do?id=1-2A881DN&ct=150219&st=sb.
  25. Landset S, Khoshgoftaar TM, Richter AN, Hasanin T. A survey of open source tools for machine learning with big data in the Hadoop ecosystem. J Big Data [Internet]. Springer International Publishing; 2015;2(1):24. Available:http://www.journalofbigdata.com/content/2/1/24.
  26. Fayyad, Piatetsky-Shapiro, Smyth Communications of the ACM,1996.
  27. Dean J, Ghemawat S. MapReduce: Simplified Data Processing on Large Clusters. Commun ACM [Internet]. 2008;51(1):1–13. Available:http://www.usenix.org/events/osdi04/tech/full_papers/dean/dean_html/.
  28. ApacheHadoop. Available:http://hadoop.apache.org/.
  29. ApacheMahout. Available:http://mahout.apache.org/.
  30. Zaharia M, Chowdhury M, Das T, Dave A. Fast and interactive analytics over Hadoop data with Spark. USENIX Login. 2012;37(4):45–51.
  31. Landset S, Khoshgoftaar TM, Richter AN, Hasanin T. A survey of open source tools for machine learning with big data in the Hadoop ecosystem. J Big Data [Internet]. Springer International Publishing; 2015;2(1):24. Available:http://www.journalofbigdata.com/content/2/1/24.
  32. https://www.hindawi.com/journals/jhe/2018/4302425/
  33. https://onlinelibrary.wiley.com/doi/full/10.1111/acem.12876
  34. Data Mining: Concepts and Techniques by Jiawei Han and MichelineKamber.
  35. DatasetAvailable:https://www.kaggle.com/asaumya/healthcare-data
  36. Big data mining using Apache Spark Available: https://insidebigdata.com/2014/10/27/data-science-101-mining-big-data-apache-spark/
  37. MapReduce: Simplified Data Processing on Large Clusters Jeffrey Dean and Sanjay Ghemawat
  38. A micropartitioning technique for massive data analysis using MapReduce S. Mohanapriya ; P. Natesan.https://www.icanotes.com/2019/04/16/a-history-of-ehr-through-the-years/
Index Terms

Computer Science
Information Sciences

Keywords

EHR machine learning data extraction data mining tools analysis of data Naïve Bayes classifier