We spoke to Zoë Thomson from thelucidproject about how music and machine learning are changing how we approach mental and neuropsychiatric health musictherapy machinelearning mentalhealth health interview
Interview conducted by Danielle Ellis, B.Sc.Jun 27 2022 insights from industryZoë ThomsonCo-founder and Chief Innovation Officer LUCID
Our system uses cutting-edge AI techniques to predict the optimal music intervention for each individual and deliver this personalized approach at scale. First, the system breaks a piece of music down into quantitative musical features and then uses deep reinforcement learning to predict the effects this piece of music will have on a listener.
However, there's a lack of research on how to most effectively personalize music interventions. Although research has demonstrated the value of personalization in music interventions, there’s little research that can help us understand or predict how an individual is likely to respond to a particular song.
Machine learning, a type of AI, has developed rapidly in recent years. How do you utilize this type of AI in your technologies, and what advantages does it provide? We use AI in two core ways. The first is to predict the responses evoked by a given piece of music in a data-driven, quantitative way.
Self-guided solutions like this are also an opportunity to support symptom management between sessions with a care provider. Moreover, since our solution is designed around real-time data collection of either self-reported symptoms or biometric signals, it provides an opportunity to collect data that can help providers in their decision-making.
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