Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department.
Pediatr Res
; 71(6): 725-31, 2012 Jun.
Article
em En
| MEDLINE
| ID: mdl-22441377
ABSTRACT
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Pediatria
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Diagnóstico por Computador
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Sistemas de Apoio a Decisões Clínicas
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Serviço Hospitalar de Emergência
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Mineração de Dados
Tipo de estudo:
Diagnostic_studies
/
Etiology_studies
/
Incidence_studies
/
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Child
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Child, preschool
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Humans
Idioma:
En
Ano de publicação:
2012
Tipo de documento:
Article