Learning causal and predictive clinical practice guidelines from data

Subramani Mani, Constantin Aliferis, Shanthi Krishnaswami, Theodore Kotchen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Clinical practice guidelines (CPG) propose preventive, diagnostic and treatment strategies based on the best available evidence. CPG enable practice of evidencebased medicine and bring about standardization of healthcare delivery in a given hospital, region, country or the whole world. This study explores generation of guidelines from data using machine learning, causal discovery methods and the domain of high blood pressure as an example.

Original languageEnglish (US)
Title of host publicationStudies in Health Technology and Informatics
Subtitle of host publicationBuilding Sustainable Health Systems
PublisherIOS Press
Pages850-854
Number of pages5
Volume129
EditionPt 2
ISBN (Print)9781586037741
StatePublished - 2007
Event12th World Congress on Medical Informatics, MEDINFO 2007 - Brisbane, QLD, Australia
Duration: Aug 20 2007Aug 24 2007

Publication series

NameStudies in Health Technology and Informatics
Volume129
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Other

Other12th World Congress on Medical Informatics, MEDINFO 2007
CountryAustralia
CityBrisbane, QLD
Period8/20/078/24/07

Keywords

  • causal discovery
  • clinical practice guidelines
  • compliance
  • high blood pressure
  • machine learning
  • prediction

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