Analysis of hospitalizations of patients affected by chronic heart disease (Contributo in atti di convegno)

Type
Label
  • Analysis of hospitalizations of patients affected by chronic heart disease (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__30 (literal)
Alternative label
  • Parodi A.; Ieva F.; Guglielmi A.; Argiento R. (2014)
    Analysis of hospitalizations of patients affected by chronic heart disease
    in First Bayesian Young Statistician Meeting (BAYSM 2013), Milano, 5-6/06/2013
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Parodi A.; Ieva F.; Guglielmi A.; Argiento R. (literal)
Pagina inizio
  • 155 (literal)
Pagina fine
  • 159 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://link.springer.com/book/10.1007%2F978-3-319-02084-6 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • The contribution of young researchers to Bayesian statistics: Proceedings of BAYSM2013 (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
  • MOX - Modeling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy; CNR-IMATI, Via Bassini, 15, 20133 Milano, Italy (literal)
Titolo
  • Analysis of hospitalizations of patients affected by chronic heart disease (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
  • In this paper we present a Bayesian model to analyze sequences of hospitalizations of patients affected by chronic heart disease, focusing not only on the sequence but also on the times between two next events; considering covariates and time, the model is able to identify the most relevant factors influencing the evolution. © Springer International Publishing Switzerland 2014. (literal)
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