Data mining techniques for analyzing stroke care processes

Silvia Panzarasa, Silvana Quaglini, Lucia Sacchi, Anna Cavallini, Giuseppe Micieli, Mario Stefanelli

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


Controlled randomized clinical trials and meta-analyses show that stroke patients benefit from access to specialized Stroke Units, in terms of mortality, disability and dependency. However, many issues relating to stroke diagnosis and therapy and to the organization of stroke care remain to be solved and little is known about what interventions make Stroke Units more effective. It is also agreed that compliance with clinical practice guidelines improves health outcomes for these patients, but little is known about the relative weight of the different guideline recommendations. Over the last decade, many hospital- or population-based stroke registers have been set up with the aim of identifying specific key indicators able to monitor the quality and adequacy of acute stroke care. Registers seem to be adequate tools for collecting the data needed to analyze care processes, providing data useful for both national healthcare planning and scientific research. In this paper we applied data mining techniques to data collected within the stroke register of the Lombardia region in Italy. From our analyses both expected and unexpected results have been found: not always compliance to recommendations is related to a good patients' outcome.

Original languageEnglish
Title of host publicationStudies in Health Technology and Informatics
Number of pages5
EditionPART 1
Publication statusPublished - 2010
Event13th World Congress on Medical and Health Informatics, Medinfo 2010 - Cape Town, South Africa
Duration: Sep 12 2010Sep 15 2010


Other13th World Congress on Medical and Health Informatics, Medinfo 2010
Country/TerritorySouth Africa
CityCape Town


  • Clinical practice guidelines
  • Data mining
  • Stroke care

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


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