{"id":609993,"date":"2019-10-02T02:30:03","date_gmt":"2019-10-02T09:30:03","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=609993"},"modified":"2024-02-28T09:10:27","modified_gmt":"2024-02-28T17:10:27","slug":"project-talia","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/project-talia\/","title":{"rendered":"Project Talia – AI for Improved Mental Health"},"content":{"rendered":"

Work on Project Talia has now been retired. We continue to actively explore the healthcare and AI space, with other projects within Microsoft Health Futures<\/a>.<\/p>\n

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One in four of us, at some point in our lives, will be affected by a mental health condition. Good mental health and well-being are fundamental to our general health and quality of life. It enables us to build resilience against everyday stresses, to work productively, to have fulfilling relationships, and to experience life as meaningful. Mental health presents one of the most challenging and under-investigated domains of machine learning research. In Project Talia we are exploring how we can best leverage AI to help improve the effectiveness of important mental health services.<\/p>\n

Collaboration with SilverCloud Health (opens in new tab)<\/span><\/a><\/h3>\n

In this project, we are collaborating with SilverCloud Health (opens in new tab)<\/span><\/a>, the leading digital therapeutics platform for mental and behavioral health. This partnership aims to jointly explore how AI can be used to enhance SilverCloud Health\u2019s digital mental health services that deliver cognitive-behavioral treatment (CBT) programs to a large and growing number of people in need of effective care. Using probabilistic machine learning frameworks, the aim is to identify new routes for personalizing treatments and improving patient engagement and clinical outcomes.<\/p>\n

More Effective Digital Mental Healthcare with AI<\/h3>\n

For improving mental health through AI, our research focuses on the following strategies:<\/p>\n\n\n\n\n\n\n\n
\"Search<\/td>\nStratify<\/strong>
\nUnderstand patient sub-types which respond best to treatment + interventions<\/td>\n<\/tr>\n
\"Icon<\/td>\nPersonalize<\/strong>
\nTailor content and delivery to achieve optimal therapy outcomes for individual patients<\/td>\n<\/tr>\n
\"Icon<\/td>\nPredict<\/strong>
\nIdentify which patients are more likely to drop-out for earlier intervention, or different programs<\/td>\n<\/tr>\n
\"Icon<\/td>\nIntervene<\/strong>
\nIntervene timely to ensure earlier intervention and improved outcomes<\/td>\n<\/tr>\n
\"Icon<\/td>\nImprove<\/strong>
\nIdentify successful patterns in supporter behaviour in relation to patient sub-type to improve therapy effectiveness<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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Events<\/h3>\n