title: Extracting knowledge from large medical databases: an automated approach.
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.
authors.affiliation: Department of Orthopaeduic Surgery, Carolinas Medical Center, Charlotte, North Carolina 28232, USA.
authors.forename: B F
authors.initials: BF
authors.lastname: Bohren
authors.forename: M
authors.initials: M
authors.lastname: Hadzikadic
authors.forename: E N
authors.initials: EN
authors.lastname: Hanley
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D001943:Breast Neoplasms; D003628:Database Management Systems / Q000145:classification / Q000458:organization & administration; D003657:Decision Making; D003661:Decision Support Techniques; D003663:Decision Trees; D005260:Female; D005544:Forecasting; D006801:Humans; D007256:Information Systems; D017116:Low Back Pain; D008499:Medical Records; D017063:Outcome Assessment, Health Care; D010363:Pattern Recognition, Automated; D014947:Wounds and Injuries