Mining the Intensive Care Unit: Knowledge

Extraction out of Medical Scoring Systems

Eirini Lygkoni & Georgios Tziralis, NTUADMINBIO 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