Project Type(s):
Clinical Research

Cognitive therapies help patients by providing ways to modify habitual but unproductive thought patterns, known as maladaptive thinking styles. Cognitive therapies are effective in treating depression, amongst other conditions, and are increasingly delivered remotely as text-based interventions. This trend toward digital delivery has accelerated on account of physical isolation and psychological stressors during the global pandemic. While this means cognitive therapy can potentially reach more patients, the effectiveness of this therapy depends on the ability of a skilled practitioner to recognize types of maladaptive thinking, and there is a critical shortage of mental health practitioners with this expertise.

In radiology, computer-aided diagnosis systems driven by artificial intelligence are used to help physicians detect signs of illness they may otherwise miss. In this project, we will develop a computer-aided detection system to support text-based cognitive therapy. To do so, we will identify indicators of maladaptive thinking styles within a set of text messages exchanged between clients and their therapists, and train neural networks to detect these indicators automatically. The resulting tools will provide a basis for an artificial intelligence-based decision support system to help clinicians recognize and manage maladaptive thinking styles that will enhance the quality and effectiveness of text-based cognitive therapy.

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