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Extracting knowledge from large medical databases: an automated approach.

B F Bohren·M Hadzikadic·E N Hanley
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Abstract

Tools which can uncover patterns in patients' records and then make predictions based on that knowledge are and will continue to be high priority in many medical informatics groups. These tools are impacting the performance of outcome studies by discovering patterns which can then be verified with standard statistical tools. This paper demonstrates INC2.5, a general classification system, as a tool for assisting physicians in the decision making process. INC2.5 gathers information from patient records and builds a decision tree which is used to assist physicians in predicting the outcome of new patients. The decision tree will also reveal any patterns which the system found in the data. Successful results of such a system can be used to enhance outcome studies as well as to spread clinical information to areas with fewer resources.

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AlgorithmsArtificial IntelligenceAutomationBreast NeoplasmsDatabase Management SystemsDecision MakingDecision Support TechniquesDecision TreesFemaleForecastingHumansInformation SystemsLow Back PainMedical RecordsOutcome Assessment, Health CarePattern Recognition, AutomatedWounds and InjuriesJournal Article

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BibTeXRIS

B F Bohren, M Hadzikadic, E N Hanley. 1995. Extracting knowledge from large medical databases: an automated approach.. https://doi.org/10.1006/cbmr.1995.1013

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