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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
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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
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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.
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Examples of Data Mining
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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. 34.5 % of all healty patients meet the conditions specified in this rule
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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%
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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.
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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
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Parameters continuously measured
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Parameters intermittently measured and the Interventions
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Some rules extracted by mining
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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.
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Discussion !! Discussion !!!
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