Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

A Bayesian Network for Probabilistic Reasoning and Imputation of Missing Risk Factors in Type 2 Diabetes

In English

Författare

  • Francesco Sambo
  • Andrea Facchinetti
  • Liisa Hakaste
  • Jasmina Kravic
  • Barbara Di Camillo
  • Giuseppe Fico
  • Jaakko Tuomilehto
  • Leif Groop
  • Rafael Gabriel
  • Tuomi Tiinamaija
  • Claudio Cobelli
Visa allt

Summary, in English

We propose a novel Bayesian network tool to model the probabilistic relations between a set of type 2 diabetes risk factors. The tool can be used for probabilistic reasoning and for imputation of missing values among risk factors. The Bayesian network is learnt from a joint training set of three European population studies. Tested on an independent patient set, the network is shown to be competitive with both a standard imputation tool and a widely used risk score for type 2 diabetes, providing in addition a richer description of the interdependencies between diabetes risk factors.

Publiceringsår

2015

Språk

Engelska

Sidor

172-176

Publikation/Tidskrift/Serie

Lecture Notes in Computer Science

Volym

9105

Dokumenttyp

Konferensbidrag

Förlag

Springer

Ämne

  • Computer Vision and Robotics (Autonomous Systems)
  • Endocrinology and Diabetes

Nyckelord

  • values imputation
  • Missing
  • Probabilistic reasoning
  • Type 2 diabetes
  • Bayesian networks

Conference name

15th Conference on Artificial Intelligence in Medicine (AIME)

Conference date

2015-06-17 - 2015-06-20

Conference place

Univ Pavia, Pavia, Italy

Status

Published

Forskningsgrupp

  • Genomics, Diabetes and Endocrinology

ISBN/ISSN/Övrigt

  • ISSN: 0302-9743
  • ISSN: 1611-3349