How AI and Machine Learning Improve Schizophrenia Research

Powerful technology can identify and measure the progression of schizophrenia.

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.

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There is no medical testing that can provide an absolute diagnosis for schizophrenia.

While early identification, diagnosis and treatment for patients with psychosis tends to mean improved outcomes, there are significant barriers to progress.

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.

The power of AI

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. They did so with 74 percent accuracy.

The research also shows an advanced 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.

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For the first time, clinicians could be able to identify and measure the progression of the disease.

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 quantitatively determine the severity of common symptoms and even identify and measure the progression of the disease, as well as the effectiveness of treatment.

There’s more where that came from

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. They will also explore ways to extend these techniques to other psychiatric disorders such as depression or post-traumatic stress disorder.

Investing 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.

This article first appeared on IBM THINK Blog and was republished with permission.

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Dr. Guillermo Cecchi is a Principal Research Staff Member for Computational Neuroscience at IBM Research.

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