AI in Mental Health



A new research collaboration seeking to break down that electronic divide with artificial intelligence-based tools, bringing together computer scientists from the Purdue University and behavioral health clinical experts. It has been estimated that around 15.5% of the population suffers from mental illness globally, and these numbers are rising continuously. There is, however, a worldwide shortage of mental health providers. This, combined with issues related to affordability and reachability, has resulted in more than 50% of the mental health patients remaining untreated. The mental health landscape became even bleaker during the COVID-19 pandemic. However, the rapid expansion of telemental health services, especially during the pandemic, has increased access to clinical care options and introduced the opportunity to use artificial intelligence (AI) based strategies to improve the quality of human-delivered mental health services. Telemental health is the process of providing psychotherapy remotely, typically utilizing HIPAA compliant video conferencing. Given that it relies a lot on technology, experienced human therapists face challenges engaging with patients due to unfamiliarity with the setup as well as other factors. For instance, in a telemental health session, the therapist has limited visual data (e.g., a therapist can only view the patient’s face rather than full body language), so the therapist has fewer non-verbal cues to guide their responses. It is also more difficult for the therapist to estimate attentiveness since eye contact required during in-person sessions is replaced with the patient looking at a camera or screen. Video conferencing discussions may also appear more stilted, especially if there is inadequate internet connection or technological challenges. Such challenges make it difficult for a therapist to perceive the patient’s several mental health indicators like engagement level, valence and arousal. Our collaboration has developed multimodal AI-based framework for modeling patient and caregive engagement and affect. This automated engagement tool will give feedback to the provider in real-time, ultimately enhancing provider engagement training and improving quality of care.

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title={DeepTMH: Multimodal Semi-supervised framework leveraging Affective and Cognitive engagement for Telemental Health},
author={Guhan, Pooja and Awasthi, Naman and Das, Ritwika and Agarwal, Manas and McDonald, Kathryn and Bussell, Kristin and Manocha, Dinesh  and Reeves, Gloria and Bera, Aniket},

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