Using Artificial Neural Networks for Identifying Patients with Mild Cognitive Impairment Associated with Depression Using Neuropsychological Test Features
| Autor: | Rafael García-Vázquez, Daniel Rivero, Juan Manuel Pías-Peleteiro, Purificación Cacabelos, José Manuel Aldrey, Santiago Rodríguez-Yáñez, Javier Andrade-Garda, Virginia Mato-Abad, Isabel Jiménez |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
medicine.medical_specialty
Neurological examination Audiology lcsh:Technology lcsh:Chemistry 03 medical and health sciences 0302 clinical medicine mild cognitive impairment medicine General Materials Science Association (psychology) Cognitive impairment lcsh:QH301-705.5 Instrumentation Depression (differential diagnoses) Fluid Flow and Transfer Processes 030214 geriatrics Artificial neural network medicine.diagnostic_test Artificial neural networks lcsh:T Vascular disease business.industry Depression Process Chemistry and Technology General Engineering food and beverages Mild cognitive impairment Cognition Neuropsychological test medicine.disease lcsh:QC1-999 MCI Computer Science Applications lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 depression neuropsychological test lcsh:Engineering (General). Civil engineering (General) business ANN lcsh:Physics 030217 neurology & neurosurgery artificial neural network |
| Zdroj: | RUC. Repositorio da Universidade da Coruña instname Applied Sciences Volume 8 Issue 9 Applied Sciences, Vol 8, Iss 9, p 1629 (2018) |
| Popis: | Depression and cognitive impairment are intimately associated, especially in elderly people. However, the association between late-life depression (LLD) and mild cognitive impairment (MCI) is complex and currently unclear. In general, it can be said that LLD and cognitive impairment can be due to a common cause, such as a vascular disease, or simply co-exist in time but have different causes. To contribute to the understanding of the evolution and prognosis of these two diseases, this study&rsquo s primary intent was to explore the ability of artificial neural networks (ANNs) to identify an MCI subtype associated with depression as an entity by using the scores of an extensive neurological examination. The sample consisted of 96 patients classified into two groups: 42 MCI with depression and 54 MCI without depression. According to our results, ANNs can identify an MCI that is highly associated with depression distinguishable from the non-depressed MCI patients (accuracy = 86%, sensitivity = 82%, specificity = 89%). These results provide data in favor of a cognitive frontal profile of patients with LLD, distinct and distinguishable from other cognitive impairments. Therefore, it should be taken into account in the classification of MCI subtypes for future research, including depression as an essential variable in the classification of a patient with cognitive impairment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|