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Synchronization of Machine Learning into Electronic Health Records

by Meet N. Gandhi, Eshan Vatsa, Nitin S. Choubey
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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.

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Index Terms

Computer Science
Information Sciences

Keywords

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

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