Decision Support System to Diagnosis and Classification of Epilepsy in Children

Authors

Abstract

Clinical decision support systems play an important role in organizations. They have
a tight relation with the information systems. Our goal is to develop a system to support the
diagnosis and the classification of epilepsy in
children. Around 50 million people in the world
have epilepsy. Epilepsy diagnosis can be an extremely complex process, demanding
considerable time and effort from physicians and healthcare infrastructures. Exams such as
electroencephalograms and magnetic resonances are often used to create a more accurate
diagnosis in a short amount of time. After the diagnosis process, physicians classify epilepsy
according to the International Classification of Diseases, ninth revision (ICD-9). Physicians
need to classify each specific type of epilepsy based on different data, e.g., types of seizures,
events and exams’ results. The classification process is time consuming and, in some cases,
demands for complementary exams. This work presents a text mining approach to support
medical decisions relating to epilepsy diagnosis and ICD-9-based classification in children. We
put forward a text mining approach using electronically processed medical records, and apply
the K-Nearest Neighbor technique as a white-box multiclass classifier approach to classify each
instance, mapping it to the corresponding ICD-9-based standard code. Results on real medical
records suggest that the proposed framework shows good performance and clear
interpretations, albeit the reduced volume of ava
ilable training data. To overcome this hurdle,
in this work we also propose and explore ways of expanding the dataset.

Journal

Journal of Universal Computer Science, Vol. 20, #6, pp. 903-927, June 2014