Healthcare
Researchers Aim To Use AI To Help Detect And Treat Bipolar Disorder
In a recent interview with the Michigan Daily, two professors of artificial intelligence – Melvin McInnis and Emily Provost – explained how AI could help people suffering from bipolar disorder.
McInnis is a professor of bipolar disorder and depression and he has researched the conditions for over 30 years. Meanwhile, Provost is an associate professor of computer science and electrical engineering. As reported by the Michigan Daily, the two researchers recently gave a talk called “Artificial Intelligence, Personalized Technology, and Mental Health” in Ann Arbor, Michigan.
McInnis and Provost are aiming to create an AI that can help diagnose sufferers of bipolar disorder. McInnis explained that one of the symptoms of bipolar disorder is speech patterns. An AI could potentially recognize subtle changes in speech patterns and facilitate the diagnosis of bipolar disorder. McInnis explains that a system that can pick up on psychological markers in speech could be used to create an early warning app that alerts sufferers and their loved ones that an episode could be impending.
Relatives of those who suffer from bipolar disorder could relax and go about their day knowing that they will be notified if signs of an impending bipolar episode have been noticed by the AI. Meanwhile, the system could help bipolar sufferers gain more independence and enable them to get prompt help once notified about a possible bipolar episode.
“Your device can give an alert and say, ‘Maybe you should talk to your doctor soon’. You can share this information with your care team, with your support network, so that you can be part of a team that’s helping you stay healthy longer.”
One of the major challenges when it comes to implementing a system that relies on detecting signs of mental illness is that cultural differences around the world can impact how signs and symptoms manifest. There will be a different baseline of “normal” for different cultures. However, if given the right training data, the AI-driven diagnosis system could hopefully compensate for these differences.
The work McInnis and Provost are doing could save lives. Catching the signs of a developing mental health crisis could potentially prevent suicide attempts, as McKinnis acknowledges that around 20% of the bipolar patients he works with end up committing suicide.
Other researchers are also experimenting with using AI to help improve the treatment and diagnosis of bipolar disorder. ZDNet recently reported that Dr. Amir Dezfouli, associated with the Commonwealth Scientific and Industrial Research Organisation (CSIRO) recently created a game powered by AI that can improve the diagnosis rate of bipolar disorder and depression. According to Dezfouli, there is currently an approximately 60% chance of misdiagnosing bipolar disease as depression, but machine learning algorithms can improve the diagnosis rate.
Dezfouli and others designed a game that monitors a patient’s behavior with metrics known to predict bipolar disorder. While these metrics can be hard for even trained clinicians to interpret, the machine learning algorithms used to analyze the data successfully reduced the misdiagnosis rate to between 20% and 40%.
Meanwhile, SilverCloud Health and Microsoft have teamed up to provide better mental health care to people online. SilverCloud is a digital mental health platform with what is currently the biggest real-world patent user-base in the world, according to PharmaTimes. SilverCloud describes itself as an evidence-based mental health service that hopes to provide its users with mental health resources in an efficient manner, giving patients clinical services at an affordable price.
Microsoft will be collaborating with SilverCloud to use machine learning and AI algorithms to enable the delivery of personalized mental healthcare for users of SilverCloud Health’s services. The algorithms that will be used on SilverCloud’s platform could enable early interventions for those who suffer from mental health conditions.
