Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department.

Introduction: 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.

Discussion: 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.