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

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Mining the Intensive Care Unit

  1. 1. Mining the Intensive Care Unit: Knowledge Extraction out of Medical Scoring Systems Eirini Lygkoni & Georgios Tziralis, NTUA DMINBIO 2009, May 08-09, Athens
  2. 2. a course by blog
  3. 3. mineknowledge
  4. 4. the problem • patients admitted to intensive care units • need to reliably monitor their status • track the expectability of overpassing their incident
  5. 5. 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)
  6. 6. scoring systems variables
  7. 7. dataset • 361 patients, *small* • women 58.9% • mean age 68.5 • death rate 11.6%
  8. 8. scoring systems distribution
  9. 9. enter data mining • 23 attributes • 2887 instances • 361 patients, > 4 days hospitalization • repeated measurements (every 3 hours) • algorithms used: OneR, C4.5, PART
  10. 10. most valuable variables
  11. 11. some rules
  12. 12. and a tree
  13. 13. 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
  14. 14. 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%
  15. 15. thank you! gtziralis@gmail.com

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