Bayesian analysis and prediction of patients' demands for visits in home care (Contributo in atti di convegno)

Type
Label
  • Bayesian analysis and prediction of patients' demands for visits in home care (Contributo in atti di convegno) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-319-02084-6__25 (literal)
Alternative label
  • Argiento R.; Guglielmi A.; Lanzarone E.; Nawajah I. (2014)
    Bayesian analysis and prediction of patients' demands for visits in home care
    in The first Bayesian Young Statisticians Meeting, BAYSM 2013, Milano, 5/6 giugno 2013
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Argiento R.; Guglielmi A.; Lanzarone E.; Nawajah I. (literal)
Pagina inizio
  • 129 (literal)
Pagina fine
  • 133 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.springer.com/statistics/statistical+theory+and+methods/book/978-3-319-02083-9 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • The contribution of young researchers to Bayesian statistics (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 63 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 63 (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-IMATI, Via Bassini 15, 20133 Milano, Italy; Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci, 32-20133 Milano, Italy (literal)
Titolo
  • Bayesian analysis and prediction of patients' demands for visits in home care (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-319-02083-9 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Ettore Lanzarone, Francesca Ieva (literal)
Abstract
  • Home care (HC) providers are complex structures which include medical, paramedical, and social services delivered to patients at their domicile. High randomness affects the service delivery, mainly in terms of unplanned changes in patients' conditions, which make the amount of required visits highly uncertain. In this paper, we propose a Bayesian model to represent the HC patient's demand evolution over time and to predict the demand in future periods. Results from the application to a relevant real case validate the approach, since low prediction errors are found. (literal)
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