IBM + University of Alberta for New Data on Machine Learning Algorithms to Help Predict Schizophrenia

At the core of stakes, we have AI and machine learning algorithms helping predict instances of schizophrenia with important accuracy. The severity of specific symptoms in schizophrenia patients with significant correlation, based on correlations between activity observed across different regions of the brain. 

We also have understanding of the neurobiology of schizophrenia; finding objective neuroimaging-based patterns that are diagnostic and prognostic markers of schizophrenia.

Examining scans from 95 participants, researchers used machine learning techniques to develop a model of schizophrenia that identifies the connections in the brain most associated with the illness.

Determine the severity of several symptoms after they have manifested in the patient, including inattentiveness, bizarre behavior and formal thought disorder, as well as alogia, (poverty of speech) and lack of motivation. 

In fact, the ultimate goal of this research effort is to identify and develop objective, data-driven measures for characterizing mental states, and apply them to psychiatric and neurological disorders.

Researchers also hope new insights into how AI and machine learning can be used to analyze psychiatric and neurological disorders to aid psychiatrists in their assessment and treatment of patients.