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

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.

Use of health information technology to reduce diagnostic errors

“…Examples of such embedded algorithms have evaluated patients for pneumonia,79 80 acute myocardial infarction,81 postoperative infections82 and, in one broad effort, to diagnose general paediatric patients with one of 18 potential conditions 83. Although several systems showed promising results, the acute myocardial infarction system did not impact decision-making in the emergency department in a pre–post evaluation,84 and we were unable to find evaluations of the use of the other systems in clinical care.…”

Estimating the average length of hospitalization due to pneumonia: a fuzzy approach

“…A similar fuzzy approach was adopted in studies on diabetic neuropathy (11), neonatal mortality (12), and evaluation of myocardial perfusion (13). Fuzzy logic tools have been applied to classify the severity of pulmonary fibrosis (14) and pneumonia in children (15) and in pediatric emergency diagnoses (16). No Brazilian studies on the effects of exposure to air pollutants and hospital respiratory disease were found; therefore, a comparison of the performance of the model presented in this study is not possible.…”

Case-Centred Multidimensional Scaling for Classification Visualisation in Medical Diagnosis

“…In our case [1], none of the single classifiers we tried showed a fully satisfactory performance. Therefore, we rely on a classifier ensemble [5] of three classifiers: a support vector machine, a neural network and a fuzzy rule-based system.…”

“…The system proposed in [1] which we use in this paper as a case study, will provide an A-and a Bdiagnosis. The A-diagnosis is the one considered to be most probable, the B-diagnosis is the one with the second highest probability.…”