BINET Business intelligence for social network analysis, case study in healthcare field (Contributo in atti di convegno)

Type
Label
  • BINET Business intelligence for social network analysis, case study in healthcare field (Contributo in atti di convegno) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Alternative label
  • Stefania Pieroni, Fabio Mariani, Paola Chiellini, Loredana Fortunato, Ernesto Lastres, Michael Liebman, Sabrina Molinaro (2011)
    BINET Business intelligence for social network analysis, case study in healthcare field
    in 9. Annual congress of international drug discovery science and technology, Shenzhen, China, November 3 - 6, 2011
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Stefania Pieroni, Fabio Mariani, Paola Chiellini, Loredana Fortunato, Ernesto Lastres, Michael Liebman, Sabrina Molinaro (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • ID_PUMA: cnr.ifc/2011-A2-013 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • 9. Annual congress of international drug discovery science and technology (IDDST) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 9 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-IFC, Pisa ; Sistemi Territoriali Srl (literal)
Titolo
  • BINET Business intelligence for social network analysis, case study in healthcare field (literal)
Abstract
  • BINET aims to design a Business Intelligence framework using Social Network technology in the healthcare field, to establish a non-conventional graph analysis platform. Scientific validation of the framework focuses on analyzing therapeutic, time and spatial associations among treatments, e.g. drug prescriptions and length of hospital stays, to find correlations between treatments of individuals and patient outcome. The validation also analyzes epidemiological and clinical databases to identify emerging technologies, standards of care, \"benchmarking\" among operational units within similar pathologies, and population profiling to identify homogeneous groups, from sociodemographics and healthcare demand, subject to tailored prevention campaigns. BINET specifically analyzes drug prescriptions for correlations between patient pathologies (derived from all treatments and diagnoses) and prescriptive behaviors of their general practitioners to identify \"guidelines\" and compare standard practices with practice guidelines. Finally the project aims at enabling the documentation of the state- of- the-practice of the research in the field. (literal)
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