Evicase: An evidence-based case structuring approach for personalized healthcare

Boaz Carmeli, Paolo Casali, Anna Goldbraich, Abigail Goldsteen, Carmel Kent, Lisa Licitra, Paolo Locatelli, Nicola Restifo, Ruty Rinott, Elena Sini, Michele Torresani, Zeev Waks

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

Abstract

The personalized medicine era stresses a growing need to combine evidence-based medicine with case based reasoning in order to improve the care process. To address this need we suggest a framework to generate multi-tiered statistical structures we call Evicases. Evicase integrates established medical evidence together with patient cases from the bedside. It then uses machine learning algorithms to produce statistical results and aggregators, weighted predictions, and appropriate recommendations. Designed as a stand-alone structure, Evicase can be used for a range of decision support applications including guideline adherence monitoring and personalized prognostic predictions.

Original languageEnglish
Title of host publicationStudies in Health Technology and Informatics
Pages604-608
Number of pages5
Volume180
DOIs
Publication statusPublished - 2012
Event24th Medical Informatics in Europe Conference, MIE 2012 - Pisa, Italy
Duration: Aug 26 2012Aug 29 2012

Other

Other24th Medical Informatics in Europe Conference, MIE 2012
Country/TerritoryItaly
CityPisa
Period8/26/128/29/12

Keywords

  • Clinical business intelligence
  • Clinical guidelines
  • Decision support
  • Machine-learning algorithms
  • Personalized medicine

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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