title: Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department.
abstract: BACKGROUND
  This article demonstrates the capacity of a combination of different data mining (DM) methods to support diagnosis in pediatric emergency patients. By using a novel combination of these DM procedures, a computer-based diagnosis was created.
  
  
  METHODS
  A support vector machine (SVM), artificial neural networks (ANNs), fuzzy logics, and a voting algorithm were simultaneously used to allocate a patient to one of 18 diagnoses (e.g., pneumonia, appendicitis). Anonymized data sets of patients who presented in the emergency department (ED) of a pediatric care clinic were chosen. For each patient, 26 identical clinical and laboratory parameters were used (e.g., blood count, C-reactive protein) to finally develop the program.
  
  
  RESULTS
  The combination of four DM operations arrived at a correct diagnosis in 98% of the cases, retrospectively. A subgroup analysis showed that the highest diagnostic accuracy was for appendicitis (97% correct diagnoses) and idiopathic thrombocytopenic purpura or erythroblastopenia (100% correct diagnoses). During the prospective testing, 81% of the patients were correctly diagnosed by the system.
  
  
  CONCLUSIONS
  The combination of these DM methods was suitable for proposing a diagnosis using both laboratory and clinical parameters. We conclude that an optimized combination of different but complementary DM methods might serve to assist medical decisions in the ED.
authors.affiliation: Department of Pediatric Haematology and Oncology, Medical University, Hannover, Germany. grigull.lorenz@mh-hannover.de
authors.forename: Lorenz
authors.initials: L
authors.lastname: Grigull
authors.forename: Werner M
authors.initials: WM
authors.lastname: Lechner
mesh_terms: D000465:Algorithms; D001064:Appendicitis / Q000175:diagnosis; D002648:Child; D002675:Child, Preschool; D015331:Cohort Studies; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004636:Emergency Service, Hospital; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D010372:Pediatrics / Q000379:methods*; D010865:Pilot Projects; D011014:Pneumonia / Q000175:diagnosis; D011446:Prospective Studies; D016553:Purpura, Thrombocytopenic, Idiopathic / Q000175:diagnosis; D012189:Retrospective Studies; D060388:Support Vector Machine