International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
•
Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
Home
Research Paper
Submit Research Paper
Publication Guidelines
Publication Charges
Upload Documents
Track Status / Pay Fees / Download Publication Certi.
Editors & Reviewers
View All
Join as a Reviewer
Reviewer Referral Program
Get Membership Certificate
Current Issue
Publication Archive
Conference
Publishing Conf. with IJFMR
Upcoming Conference(s) ↓
WSMCDD-2025
GSMCDD-2025
Conferences Published ↓
RBS:RH-COVID-19 (2023)
ICMRS'23
PIPRDA-2023
Contact Us
Plagiarism is checked by the leading plagiarism checker
Call for Paper
Volume 6 Issue 5
September-October 2024
Indexing Partners
From EHRs to Insights: How Machine Learning is Transforming Healthcare Data Management
Author(s) | Ginoop Chennekkattu Markose |
---|---|
Country | United States |
Abstract | The care industry is in the middle of a transformation because of the adoption of EHRs and the integration of ML into the framework of healthcare. This article also has the intention of discussing how ML assists in changing the management of healthcare information and, as such, indicates how the raw EHR data can be utilized. Some of the traditional challenges associated with handling immensely large, diverse and geographically distributed healthcare data have been solved by employments of Maxims and the use of intelligence algorisms to encompass data manipulation, pattern recognition and Information content anticipation. First of all, they have not only used the new opportunities to provide better, improved and more efficient services to the patients but also in the area of cost-cutting and introduction of the system of personalized medicine. In this paper, the author explored the place that ML occupies in consideration of the management of healthcare data with reference to techniques such as supervised learning, unsupervised learning, NLP and deep learning. They are then presented with regard to uses such as patient risk assessment, clinical decision-making, and population health. Furthermore, the paper also reveals that the challenges of ‘bringing’ ML into healthcare include the issues of data privacy and ethical questions regarding the data governance efforts needed. The study also proceeds further to talk about the future of ML in healthcare with regard to predictive and precision medicine. Some of the other interdisciplinary integration of ML is a combination of the technology with other currently dominant technologies like blockchain or IoT, where the integration of these two with ML is demonstrated, and other possibilities in the management of healthcare data are explored. In support of the said arguments the article gives instances of cases of the effective application of ML in the health sector as well as giving out tables and figures. To that end, it is important to assert that more research should be done. More monetary investment is made in the development of ML technologies so that these concepts can be better implemented and optimized. These two observations can become more fully realized in their potential to revolutionize the ways in which healthcare information is managed by healthcare providers, technologists and policymakers in the future and now. |
Keywords | Machine Learning (ML), Electronic Health Records (EHRs), Healthcare Data Management, Predictive Analytics, Natural Language Processing (NLP), Personalized Medicine, Data Privacy. |
Field | Computer > Artificial Intelligence / Simulation / Virtual Reality |
Published In | Volume 6, Issue 5, September-October 2024 |
Published On | 2024-09-12 |
Cite This | From EHRs to Insights: How Machine Learning is Transforming Healthcare Data Management - Ginoop Chennekkattu Markose - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.27377 |
DOI | https://doi.org/10.36948/ijfmr.2024.v06i05.27377 |
Short DOI | https://doi.org/gwfgfv |
Share this
E-ISSN 2582-2160
doi
CrossRef DOI is assigned to each research paper published in our journal.
IJFMR DOI prefix is
10.36948/ijfmr
Downloads
All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.