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Machine Learning for Digital Shadow Design in Health Insurance Sector
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Machine Learning for Digital Shadow Design in Health Insurance Sector
Journal
Mobile Networks and Applications
ISSN
1383-469X
1572-8153
Publisher
Springer Nature
Date Issued
2024
Author(s)
Rodríguez Aguilar, Román
Facultad de Ciencias Económicas y Empresariales - CampCM
Marmolejo Saucedo, José Antonio
Facultad de Ingeniería - CampCM
Rodríguez-Aguilar, Miriam
Marmolejo-Saucedo, Liliana
Type
Resource Types::text::journal::journal article
DOI
10.1007/s11036-023-02289-2
URL
https://scripta.up.edu.mx/handle/123456789/9965
Abstract
The digital transformation process in organizations has accelerated significantly in recent years; the COVID-19 pandemic was a catalyst that highlighted the need for digitalization in all sectors. In the case of the health sector, this process is complex due to the processes inherent in health care as well as the integration of multiple sectors that allow the provision of health services. A first approach towards the construction of a Digital Twin in health organizations is a Digital Shadow that allows an orderly transition towards digital operation in real time. This paper presents a first approach to the design of a Digital Shadow for the health insurance sector and specifically for the care of patients diagnosed with COVID-19 through the implementation of an analytical intelligence system based on machine learning models to forecast and monitor to patients who represent catastrophic cases for the insurer. © 2024 Springer Nature
Subjects
Digitalization in hea...
Digital shadow ·
Machine learning
COVID-19
Health insurance
How to cite
Rodríguez-Aguilar, R., Marmolejo-Suacedo, JA., Rodríguez-Aguilar, M. et al. Machine Learning for Digital Shadow Design in Health Insurance Sector. Mobile Netw Appl (2024). https://doi.org/10.1007/s11036-023-02289-2
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Jul 21, 2024
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