Development Data of Mood Inference Engine


AIHW 2018 reports mental health issues as the leading cause of burden in young working-age adults (25-44). Encouragingly, research shows that early detection and intervention can save 60% of hospitalisations, potentially saving almost 500 million in Queensland's economy. The proposed research is focused on the early detection of mood changes that are typical of relapse through innovative digital methods, enabling early intervention. The outcomes of this project will include the development of a tool to automatically determine mood simply from day-to-day phone conversations on a smartphone and a system for early diagnosis of relapse by tracking mood in real-time. The relapse prediction system takes the mood as input and can aid in relapse prevention by keeping the patients aware of their prolonged negative mood, allowing them to seek help on time and by making clinicians aware of a potential relapse enabling early interventions.

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Rana, R. Development Data of Mood Inference Engine. [Collection Record (Dataset)]. doi.org/10.26192/PV1J-E485

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