The role of depression in mild cognitive impairment through the analysis with artificial neural networks

2018 ◽  
Author(s):  
Virginia Mato-Abad ◽  
Isabel Jiménez ◽  
Francisco Cedrón Santaeufemia ◽  
Juan M. Pías-Peleteiro ◽  
Purificación Cacabelos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Artificial Neural

scholarly journals Using Artificial Neural Networks for Identifying Patients with Mild Cognitive Impairment Associated with Depression Using Neuropsychological Test Features

2018 ◽  
Vol 8 (9) ◽  
pp. 1629 ◽  
Author(s):  
Virginia Mato-Abad ◽  
Isabel Jiménez ◽  
Rafael García-Vázquez ◽  
José Aldrey ◽  
Daniel Rivero ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Neuropsychological Test ◽  
Future Research ◽  
Late Life Depression ◽  
Essential Variable ◽  
Artificial Neural

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’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.

Technique of scientific and technical forecasting using neural networks

2018 ◽  
pp. 103-110
Author(s):  
V. V. Nefedev
Keyword(s):  
Mathematical Modeling ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Original Method ◽  
Forecasting Methods ◽  
Expert Assessments ◽  
Artificial Neural

For the definition and implementation of breakthrough technologies the most important is the role of scientific and technical forecasting. Well-known forecasting methods based on extrapolation, expert assessments and mathematical modeling are not universal and have a number of significant disadvantages. The article proposes an original method of scientific and technical forecasting based on the use of the methodology of artificial neural networks. 

scholarly journals Cardiovascular events in patients with mild autonomous cortisol secretion: analysis with artificial neural networks

2017 ◽  
Vol 177 (1) ◽  
pp. 73-83 ◽  
Author(s):  
Valentina Morelli ◽  
Serena Palmieri ◽  
Andrea Lania ◽  
Alberto Tresoldi ◽  
Sabrina Corbetta ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Characteristic Curve ◽  
Smoking Habit ◽  
Adrenal Incidentaloma ◽  
Cortisol Secretion ◽  
Autonomous Cortisol Secretion ◽  
Artificial Neural

Background The independent role of mild autonomous cortisol secretion (ACS) in influencing the cardiovascular event (CVE) occurrence is a topic of interest. We investigated the role of mild ACS in the CVE occurrence in patients with adrenal incidentaloma (AI) by standard statistics and artificial neural networks (ANNs). Methods We analyzed a retrospective record of 518 AI patients. Data regarding cortisol levels after 1 mg dexamethasone suppression (1 mg DST) and the presence of obesity (OB), hypertension (AH), type-2 diabetes (T2DM), dyslipidemia (DL), familial CVE history, smoking habit and CVE were collected. Results The receiver-operating characteristic curve analysis suggested that 1 mg DST, at a cut-off of 1.8 µg/dL, had the best accuracy for detecting patients with increased CVE risk. In patients with 1 mg-DST ≥1.8 µg/dL (DST+, n = 223), age and prevalence of AH, T2DM, DL and CVE (66 years, 74.5, 25.9, 41.4 and 26.8% respectively) were higher than that of patients with 1 mg-DST ≤1.8 µg/dL (61.9 years, 60.7, 18.5, 32.9 and 10%, respectively, P < 0.05 for all). The CVE were associated with DST+ (OR: 2.46, 95% CI: 1.5–4.1, P = 0.01), regardless of T2DM, AH, DL, smoking habit, gender, observation period and age. The presence of at least two among AH, T2DM, DL and OB plus DST+ had 61.1% sensitivity in detecting patients with CVE. By using the variables selected by ANNs (familial CVE history, age, T2DM, AH, DL and DST+) 78.7% sensitivity was reached. Conclusions Cortisol after 1 mg-DST is independently associated with the CVE occurrence. The ANNs might help for assessing the CVE risk in AI patients.

Sign in / Sign up

Export Citation Format

Share Document