Technology Management for Accelerated Recovery during COVID-19: A Data-Driven Machine Learning Approach

SEISENSE Journal of Management

View Publication Info
 
 
Field Value
 
Title Technology Management for Accelerated Recovery during COVID-19: A Data-Driven Machine Learning Approach
 
Creator Morande, Swapnil
Tewari, Veena
 
Subject Machine Learning
Artificial Intelligence
Healthcare
Predictive Analytics
COVID-19
 
Description Objective- The research looks forward to extracting strategies for accelerated recovery during the ongoing Covid-19 pandemic.
Design - Research design considers quantitative methodology and evaluates significant factors from 170 countries to deploy supervised and unsupervised Machine Learning techniques to generate non-trivial predictions.
Findings - Findings presented by the research reflect on data-driven observation applicable at the macro level and provide healthcare-oriented insights for governing authorities.
Policy Implications - Research provides interpretability of Machine Learning models regarding several aspects of the pandemic that can be leveraged for optimizing treatment protocols.
Originality - Research makes use of curated near-time data to identify significant correlations keeping emerging economies at the center stage. Considering the current state of clinical trial research reflects on parallel non-clinical strategies to co-exist with the Coronavirus.
 
Publisher SEISENSE (PRIVATE) LIMITED
 
Date 2020-09-06
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer Reviewed Article
 
Format application/pdf
 
Identifier http://journal.seisense.com/index.php/jom/article/view/445
10.33215/sjom.v3i5.445
 
Source SEISENSE Journal of Management; Vol. 3 No. 5 (2020): SEISENSE Journal of Management; 33-53
2617-5770
 
Language eng
 
Relation http://journal.seisense.com/index.php/jom/article/view/445/124
 
Rights Copyright (c) 2020 Swapnil Morande, Dr. Veena Tewari
https://creativecommons.org/licenses/by/4.0
 

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