1. Multimodal diagnosis of epilepsy using conditional dependence and multiple imputation
- Author
-
Jerome Engel, Justine M. Le, Eric S. Hwang, Andrew Y. Cho, Chelsea T. Braesch, Emily A. Janio, Mark S. Cohen, Edward Lau, Emily C. Davis, Wesley T. Kerr, John M. Stern, Akash B. Patel, Sarah E. Barritt, Daniel H.S. Silverman, Ariana Anderson, Jessica M. Hori, Kaavya R. Raman, and Noriko Salamon
- Subjects
Decision tree ,Neurodegenerative ,Electroencephalography ,Machine learning ,computer.software_genre ,Article ,Epilepsy ,Text mining ,Neuroimaging ,Clinical Research ,medicine ,Medical diagnosis ,Conditional dependence ,medicine.diagnostic_test ,business.industry ,Neurosciences ,Pattern recognition ,Missing data ,medicine.disease ,Brain Disorders ,Neurological ,Biomedical Imaging ,Artificial intelligence ,Psychology ,business ,computer - Abstract
The definitive diagnosis of the type of epilepsy, if it exists, in medication-resistant seizure disorder is based on the efficient combination of clinical information, long-term video-electroencephalography (EEG) and neuroimaging. Diagnoses are reached by a consensus panel that combines these diverse modalities using clinical wisdom and experience. Here we compare two methods of multimodal computer-aided diagnosis, vector concatenation (VC) and conditional dependence (CD), using clinical archive data from 645 patients with medication-resistant seizure disorder, confirmed by video-EEG. CD models the clinical decision process, whereas VC allows for statistical modeling of cross-modality interactions. Due to the nature of clinical data, not all information was available in all patients. To overcome this, we multiply-imputed the missing data. Using a C4.5 decision tree, single modality classifiers achieved 53.1%, 51.5% and 51.1% average accuracy for MRI, clinical information and FDG-PET, respectively, for the discrimination between nonepileptic seizures, temporal lobe epilepsy, other focal epilepsies and generalized-onset epilepsy (vs. chance, p
- Published
- 2014
- Full Text
- View/download PDF