Predictive Model of Mental Disorders – Schizophrenia 

University of Alberta and IBM TJ Watson Research


  • Dr. Russ Greiner, Computing Science, University of Alberta
  • University of Alberta, Department of Psychiatry
  • IBM Thomas J. Watson Research Center, Yorktown Heights (Computational Biology), NY

Following the success of Phase 1, the project will continue to advance the analysis of fMRI brain data images with machine learning techniques to diagnose and predict schizophrenia.

Schizophrenia Project updates, news and a list of collaborators can be found on the Computational Psychiatry website

A project report can be viewed at this link:

Pioneering research in “computational psychiatry” uses AI to explore disease prediction and assessment with 74 per cent accuracy

January 2019 project update: Paper published in npj Schizophrenia:

Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning

Click to see article

January 2019 project update 2: Canadian and World Media Response to npj Schizophrenia Article:

“Scientists at the University of Alberta are finding ways for artificial intelligence to spot schizophrenia in brain scans” – The Star

“Patient brain scans are being used to identify schizophrenia, a diagnosis historically reliant on subjective data of patient experiences, say University of Alberta researchers. ” – Edmonton Journal

“Breakthrough in diagnosing schizophrenia using historical data.” – 630 CHED

“Using Artificial Intelligence to predict Schizophrenia” – Research Matters

“Nimhans partners Canadian varsity to build AI capabilities for schizophrenia diagnosis” – TechCircle

“Improved AI-based tool increases accuracy of schizophrenia diagnosis” – Folio

“New AI tool increases accuracy of schizophrenia diagnosis” – Times of India

“Scientists develop AI-powered tool to accurately diagnose schizophrenia” – International Business Times

Sunil Kalmady (centre), Russ Greiner (left), Andrew Greenshaw (right)