Semiparametric Bayesian models for clustering and classification in the presence of unbalanced in-hospital survival (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• Semiparametric Bayesian models for clustering and classification in the presence of unbalanced in-hospital survival (Articolo in rivista) (literal)

Anno

• 2014-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1111/rssc.12021 (literal)

Alternative label

• Guglielmi, Alessandra; Ieva, Francesca; Paganoni, Anna M.; Ruggeri, Fabrizio; Soriano, Jacopo (2014)
  Semiparametric Bayesian models for clustering and classification in the presence of unbalanced in-hospital survival
  in Applied statistics (Print)
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Guglielmi, Alessandra; Ieva, Francesca; Paganoni, Anna M.; Ruggeri, Fabrizio; Soriano, Jacopo (literal)

Pagina inizio

• 25 (literal)

Pagina fine

• 46 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://onlinelibrary.wiley.com/doi/10.1111/rssc.12021/abstract (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 63 (literal)

Rivista

• Applied statistics (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

• 22 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

• 1 (literal)

Note

• ISI Web of Science (WOS) (literal)
• Scopu (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• Polytechnic University of Milan; Consiglio Nazionale delle Ricerche (CNR); Duke University (literal)

Titolo

• Semiparametric Bayesian models for clustering and classification in the presence of unbalanced in-hospital survival (literal)

Abstract

• Bayesian semiparametric logit models are fitted to grouped data related to in-hospital survival outcome of patients hospitalized with an ST-segment elevation myocardial infarction diagnosis. Dependent Dirichlet process priors are considered for modelling the random-effects distribution of the grouping factor (hospital of admission), to provide a cluster analysis of the hospitals. The clustering structure is highlighted through the optimal random partition that minimizes the posterior expected value of a suitable loss function. There are two main goals of the work: to provide model-based clustering and ranking of the providers according to the similarity of their effect on patients' outcomes, and to make reliable predictions on the survival outcome at the patient's level, even when the survival rate itself is strongly unbalanced. The study is within a project, named the Strategic program of Regione Lombardia', and is aimed at supporting decisions in healthcare policies. (literal)

Prodotto di

• Istituto di matematica applicata e tecnologie informatiche \"Enrico Magenes\" (IMATI) (Istituto)
• Metodi statistici per problemi biomedici (ME.P07.004.002) (Modulo)

Autore CNR

• FABRIZIO RUGGERI (Persona)

Insieme di parole chiave

• Parole chiave di "Semiparametric Bayesian models for clustering and classification in the presence of unbalanced in-hospital survival" (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto di matematica applicata e tecnologie informatiche \"Enrico Magenes\" (IMATI) (Istituto)
• Metodi statistici per problemi biomedici (ME.P07.004.002) (Modulo)

Autore CNR di

• FABRIZIO RUGGERI (Persona)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Applied statistics (Rivista)

Insieme di parole chiave di

• Parole chiave di "Semiparametric Bayesian models for clustering and classification in the presence of unbalanced in-hospital survival" (Insieme di parole chiave)