Alcohol Addiction: One Entity or Different Entities? A DSM-4-Based Attempt Toward a Geographicization of Alcohol Addiction and Abuse.

Aim: To examine whether in Europe perceptions of 'alcoholism' differ in a discrete manner according to geographical area.

Method: Secondary analysis of a data set from a European project carried out in 2013-2014 among 1767 patients treated in alcohol addiction units of nine countries/regions across Europe. The experience of all 11 DSM-4 criteria used for diagnosing 'alcohol dependence' and 'alcohol abuse' were assessed in patient interviews.

Assessment of New Coronary Features on Quantitative Coronary Angiographic Images With Innovative Unsupervised Artificial Adaptive Systems: A Proof-of-Concept Study.

The Active Connection Matrixes (ACMs) are unsupervised artificial adaptive systems able to extract from digital images features of interest (edges, tissue differentiation, etc.) unnoticeable with conventional systems. In this proof-of-concept study, we assessed the potentiality of ACMs to increase measurement precision of morphological structures (e.

Artificial Neural Networks: an overview and their use in the analysis of the AMPHORA-3 dataset.

The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to create artificial models simulating natural phenomenon. Artificial Neural Networks (ANNs) are the more diffused and best-known learning system models in the AAS. This article describes an overview of ANNs, noting its advantages and limitations for analyzing dynamic, complex, non-linear, multidimensional processes.

Two different Alzheimer diseases in men and women: clues from advanced neural networks and artificial intelligence.

Background: Studies of the gender-related differences in the clinical presentation of Alzheimer's disease (AD) have focused on specific aspects of the disease (eg, circulating metabolites, cognitive capacity, or epidemiologic trends).

Objective: This study accounts for several descriptors of the disease simultaneously, providing a multidimensional analysis of a cohort of patients with AD.

Methods: Our analysis was conducted using self-organizing maps (SOMs).

Artificial neural networks and artificial organisms can predict Alzheimer pathology in individual patients only on the basis of cognitive and functional status.

Data from several studies have pointed out the existence of a strong correlation between Alzheimer's disease (AD) neuropathology and cognitive state. However, because of their highly complex and nonlinear relationship, it has been difficult to develop a predictive model for individual patient classification through traditional statistical approaches. When exposed to complex data sets, artificial neural networks (ANNs) can recognize patterns, learn the relationship of different variables, and address classification tasks.