Mining the Intensive Care Unit
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Mining the Intensive Care Unit

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A research paper's presentation at the "Data Mining in Bioinformatics" conference, that took place in 7-8 May in Athens, Greece

A research paper's presentation at the "Data Mining in Bioinformatics" conference, that took place in 7-8 May in Athens, Greece

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Mining the Intensive Care Unit Mining the Intensive Care Unit Presentation Transcript

  • Mining the Intensive Care Unit: Knowledge Extraction out of Medical Scoring Systems Eirini Lygkoni & Georgios Tziralis, NTUA DMINBIO 2009, May 08-09, Athens
  • a course by blog
  • mineknowledge
  • the problem • patients admitted to intensive care units • need to reliably monitor their status • track the expectability of overpassing their incident
  • given solution • Scoring Systems - tracking the heaviness of an ilness • APACHE II (Acute Physiology and Chronic Health Evaluation) • APACHE III • SAPS II (Simplified Acute Physiology Score) • SOFA (Sequential Organ Failure Score)
  • scoring systems variables
  • dataset • 361 patients, *small* • women 58.9% • mean age 68.5 • death rate 11.6%
  • scoring systems distribution
  • enter data mining • 23 attributes • 2887 instances • 361 patients, > 4 days hospitalization • repeated measurements (every 3 hours) • algorithms used: OneR, C4.5, PART
  • most valuable variables
  • some rules
  • and a tree
  • discussion • introduced a novel approach to assessing the status of patients in intensive care unit • quality results with less variables needed • more easily comprehensible & discrete outcomes • though maybe need to combine them somehow
  • future work • more extended, generalizable dataset needed • formalization of a simplified and more descriptive new scoring system, out of mining outcomes • reach an accuracy rate close to 100%
  • thank you! gtziralis@gmail.com