Applying artificial intelligence to identify schizophrenia

The mental health disorder schizophrenia has been associated with disrupted brain connectivity. One way of identifying this is through study of specific neuroimaging-based patterns, which are medically described as “pathognomonic” when they indicate schizophrenia. Moreover, such patterns can offer cues about the degree of severity of the symptoms. The task, however, is heavily data reliant and requires complex analysis. The complexity is down to the analysis not being simply due to looking for patterns of significance. Reviews also need to process data drawn from multiple datasets, contexts and cohorts. Schizophrenia is a mental disorder characterized by abnormal social behavior and failure to understand what is real. A combination of genetic and environmental factors are considered to play a role in the development of schizophrenia. Symptoms of schizophrenia are divided into ‘positive’ and ‘negative’. Positive symptoms include experiencing things that are not real (hallucinations) and having unusual beliefs (delusions); whereas negative symptoms include may be a lack of motivation and being withdrawn. They often last longer than positive symptoms. To help improve the accuracy of predicting schizophrenia research from the Department of Computing Science at the University of Alberta in Edmonton, Canada has developed a predictive method based on the analysis…

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