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