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Predictive modeling for dengue patient�s length of stay (LoS) using big data analytics (BDA)

Big data; Data Analytics; Decision support systems; Health care; Demographic data; Descriptive analysis; Healthcare facility; Hospital costs; Length of stay; Modeling technique; Predictive modeling; Resource planning; Predictive analytics

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Bibliographic Details
Main Authors: Hendri H.J.M., Sulaiman H.
Other Authors: 57218831669
Format: Book Chapter
Published: Springer Science and Business Media Deutschland GmbH 2023
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id my.uniten.dspace-23937
record_format dspace
spelling my.uniten.dspace-239372023-05-29T14:53:18Z Predictive modeling for dengue patient�s length of stay (LoS) using big data analytics (BDA) Hendri H.J.M. Sulaiman H. 57218831669 54903312800 Big data; Data Analytics; Decision support systems; Health care; Demographic data; Descriptive analysis; Healthcare facility; Hospital costs; Length of stay; Modeling technique; Predictive modeling; Resource planning; Predictive analytics Big data analytics (BDA) in healthcare has become increasingly popular as it offers numerous benefits healthcare stakeholders including physicians, management and insurers. By using dengue epidemic as a case, we identified patient�s length of stay (LoS) as a parameter for the efficiency of care and potentially optimize hospital costs. This paper reports findings from two healthcare facilities based in Malaysia, which recorded 9,261 dengue patients in the year 2014. The main purpose of this study is to provide descriptive analysis and propose big data analytics modeling technique to determine and predict LoS of dengue patients. Demographic data such as age, gender, admission and discharge date have been identified as factors that contribute to the prediction of LoS. The suggested predictive modeling technique may improve resource planning through the use of simple decision support system. Recommendations of this study may also assist the expectation of healthcare facilities on their patient�s LoS. � Springer International Publishing AG 2018. Final 2023-05-29T06:53:18Z 2023-05-29T06:53:18Z 2018 Book Chapter 10.1007/978-3-319-59427-9_2 2-s2.0-85090369863 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090369863&doi=10.1007%2f978-3-319-59427-9_2&partnerID=40&md5=f983388cb0df33c33f715ddf0c1eed40 https://irepository.uniten.edu.my/handle/123456789/23937 5 12 19 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Big data; Data Analytics; Decision support systems; Health care; Demographic data; Descriptive analysis; Healthcare facility; Hospital costs; Length of stay; Modeling technique; Predictive modeling; Resource planning; Predictive analytics
author2 57218831669
author_facet 57218831669
Hendri H.J.M.
Sulaiman H.
format Book Chapter
author Hendri H.J.M.
Sulaiman H.
spellingShingle Hendri H.J.M.
Sulaiman H.
Predictive modeling for dengue patient�s length of stay (LoS) using big data analytics (BDA)
author_sort Hendri H.J.M.
title Predictive modeling for dengue patient�s length of stay (LoS) using big data analytics (BDA)
title_short Predictive modeling for dengue patient�s length of stay (LoS) using big data analytics (BDA)
title_full Predictive modeling for dengue patient�s length of stay (LoS) using big data analytics (BDA)
title_fullStr Predictive modeling for dengue patient�s length of stay (LoS) using big data analytics (BDA)
title_full_unstemmed Predictive modeling for dengue patient�s length of stay (LoS) using big data analytics (BDA)
title_sort predictive modeling for dengue patient�s length of stay (los) using big data analytics (bda)
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806427567015591936
score 13.214268

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