StatMaster – An Update Kartik Vishwanath Chintan Patel Yugyung Lee UMKC William Drake Richard Stroup Steve Simon Childrens Mercy Hospital, Kansas City,

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Presentation transcript:

StatMaster – An Update Kartik Vishwanath Chintan Patel Yugyung Lee UMKC William Drake Richard Stroup Steve Simon Childrens Mercy Hospital, Kansas City, MO 07 June 2004

Defining Data Mining The automated extraction of hidden predictive information from (large) databases Three key words: Automated Hidden Predictive Implicit is a statistical methodology Data mining lets you be proactive Prospective rather than Retrospective

Kinds of Data Mining Problems Classification: Finding a set of models that describe or distinguish data classes. Clustering: Grouping objects by minimizing interclass similarity and maximizing intraclass similarity. Association: Discovery of association rules showing attribute-value conditions that occur frequently.

Examples of Data Mining

A Healthy Class Rule for the Cardiology Patient Dataset IF 169 <= Maximum Heart Rate <=202 THEN Concept Class = Healthy Rule accuracy: 85.07% Rule coverage: 34.55% The rule works correctly 85% of the time % of all healty patients meet the conditions specified in this rule

A Sick Class Rule for the Cardiology Patient Dataset IF Thal = Rev & Chest Pain Type = Asymptomatic THEN Concept Class = Sick Rule accuracy: 91.14% Rule coverage: 52.17%

Drawing Conclusions Recall the rule: IF 169 <= Maximum Heart Rate <=202 THEN Concept Class = Healthy Possible interpretations If patient’s max heart rate is low, s/he might have a heart attack? If patient had a heart attack, his max heart rate would decrease? A low max heart rate causes a heart attack? Only a medical expert can tell.

Another Example Hypoplastic Left Heart Syndrome Case Study Affects infants and is uniformly fatal without surgery. Extremely complex relationships among physiologic parameters in a given patient. Temporal datasets

Parameters continuously measured

Parameters intermittently measured and the Interventions

Some rules extracted by mining

Results Wellness score predicted with accuracy of 94.57%. Incorrect predictions for 1.60% of new cases (with unknown value of the wellness score) 2.22% of new cases the decision rules could not make any predications.

Discussion !! Discussion !!!