Artificial Intelligence

How AI and Machine Learning are Aiding Schizophrenia Research

Share this post:

In the U.S. about 20 percent of adults suffer from a mental health condition, ranging from depression to bipolar disorder to schizophrenia, and about half of those with severe psychiatric disorders receive no treatment. While early identification, diagnosis, and treatment for patients with psychosis tends to mean improved outcomes, there continues to be significant barriers in achieving this. For schizophrenia, there is no medical testing that can provide an absolute diagnosis; this can mean significant delay before a symptomatic person is successfully diagnosed.

A computer model of the human brain, a “brain template.”

Earlier this year, IBM scientists collaborated with researchers at the University of Alberta and the IBM Alberta Centre for Advanced Studies (CAS) to publish new research regarding the use of AI and machine learning algorithms to predict instances of schizophrenia with a 74 percent accuracy. The research also shows a further capability to predict the severity of specific symptoms in schizophrenia patients – something that was not possible before. Using AI and machine learning, ‘computational psychiatry’ can be used to help clinicians more quickly assess – and therefore treat – patients with schizophrenia.

Computational psychiatry provides physicians with tools that enable them to objectively assess patients where most approaches had been subjective up until that point. In this schizophrenia research, we have learned that powerful technology can be used to predict the likelihood of a previously-unseen patient having schizophrenia. For the first time, clinicians could be able to quantitatively determine the severity of common symptoms and even identify and measure the progression of the disease, as well as the effectiveness of treatment.

This kind of innovative collaboration is just one example of the work being done between IBM and the University of Alberta through the IBM Alberta Centre for Advanced Studies. For more than a decade, the Centre’s unique public/private approach to research has become an example at a global level of how teaming world-class scientists and researchers can drive greater discovery and progression of disruptive technologies to address some of our greatest challenges.

As part of the ongoing relationship, research teams will continue to investigate areas and connections in the brain that hold significant links to schizophrenia, and also explore ways to extend these techniques to other psychiatric disorders, such as depression or post-traumatic stress disorder.

IBM has always recognized that investment in research and development is an important driver in solving some of our greatest global health problems, and this research is indicative of that commitment. It is a real example of innovation that matters.

Principal Research Staff Member, Computational Neuroscience, IBM Research

More Artificial Intelligence stories

Fast Data Ingestion, ML Equates to Smarter Decisions Faster

Human beings tend to filter out events they deem unimportant to avoid sensory overload. They can only process so much at any given time. Computer systems, however, must be able to handle a massive number of digital “events” – everything from changes in your car’s engine to millions of retail transactions – in real time or […]

Continue reading

Empowering the New Data Developer

After years of frustration with the trucking industry’s slow and inconsistent processes for loading and unloading cargo, Malcolm McLean in 1956 watched as his SS Ideal-X left port in New Jersey loaded with 58 of the world’s first intermodal shipping containers – a product he invented and patented. The defining feature of his container was […]

Continue reading

Unifying Data Governance for the Future

Mastering fast-growing data volumes across the enterprise is one of the first and most critical steps in establishing a cognitive business. To do it requires adopting advanced analytics that enable an organization to better understand and control its data, but also to gain insights that set the stage for driving new business models. But for […]

Continue reading