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Pediatr Res. 2012 Jun;71(6):725-31. doi: 10.1038/pr.2012.34. Epub 2012 Mar 22.

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

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Department of Pediatric Haematology and Oncology, Medical University, Hannover, Germany.



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.


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.


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.


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.

[Indexed for MEDLINE]

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