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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

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Mining the Intensive Care Unit