A Predictive Model for Acute Admission in Aged Population

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Abstract

Acute hospital admission among the elderly population is very common and have a high impact on the health services and the community, as well as on the individuals. Several studies have focused on the possible risk factors, however, predicting who is at risk for acute hospitalization associated with disease and symptoms is still an open research question. In this study, we investigate the use of machine learning algorithms for predicting acute admission in older people based on admission data from individual citizens 70 years and older who were hospitalized in the acute medical unit of Svendborg Hospital in Denmark.

Original languageEnglish
Title of host publicationBuilding Continents of Knowledge in Oceans of Data : The Future of Co-Created eHealth
EditorsAdrien Ugon, Daniel Karlsson, Gunnar O. Klein, Anne Moen
PublisherIOS Press
Publication date2018
Pages96-100
ISBN (Print)978161499858
ISBN (Electronic)9781614998525
DOIs
Publication statusPublished - 2018
SeriesStudies in Health Technology and Informatics
Volume247
ISSN0926-9630

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Hospital Units
Denmark
Health Services
Research
Population

Keywords

  • Acute admission
  • Data science
  • Healthcare
  • Machine learning
  • Predictive model

Cite this

Mansourvar, M., Andersen-Ranberg, K., Nøhr, C., & Wiil, U. K. (2018). A Predictive Model for Acute Admission in Aged Population. In A. Ugon, D. Karlsson, G. O. Klein, & A. Moen (Eds.), Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth (pp. 96-100). IOS Press. Studies in Health Technology and Informatics, Vol.. 247 https://doi.org/10.3233/978-1-61499-852-5-96
Mansourvar, Marjan ; Andersen-Ranberg, Karen ; Nøhr, Christian ; Wiil, Uffe Kock. / A Predictive Model for Acute Admission in Aged Population. Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth. editor / Adrien Ugon ; Daniel Karlsson ; Gunnar O. Klein ; Anne Moen. IOS Press, 2018. pp. 96-100 (Studies in Health Technology and Informatics, Vol. 247).
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Mansourvar, M, Andersen-Ranberg, K, Nøhr, C & Wiil, UK 2018, A Predictive Model for Acute Admission in Aged Population. in A Ugon, D Karlsson, GO Klein & A Moen (eds), Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth. IOS Press, Studies in Health Technology and Informatics, vol. 247, pp. 96-100. https://doi.org/10.3233/978-1-61499-852-5-96

A Predictive Model for Acute Admission in Aged Population. / Mansourvar, Marjan; Andersen-Ranberg, Karen; Nøhr, Christian; Wiil, Uffe Kock.

Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth. ed. / Adrien Ugon; Daniel Karlsson; Gunnar O. Klein; Anne Moen. IOS Press, 2018. p. 96-100 (Studies in Health Technology and Informatics, Vol. 247).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Mansourvar M, Andersen-Ranberg K, Nøhr C, Wiil UK. A Predictive Model for Acute Admission in Aged Population. In Ugon A, Karlsson D, Klein GO, Moen A, editors, Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth. IOS Press. 2018. p. 96-100. (Studies in Health Technology and Informatics, Vol. 247). https://doi.org/10.3233/978-1-61499-852-5-96