Data

Development Data of Mood Inference Engine

University of Southern Queensland
Dr Rajib Rana (Owned by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doidoi.org/10.26192/PV1J-E485&rft.title=Development Data of Mood Inference Engine&rft.identifier=doi.org/10.26192/PV1J-E485&rft.publisher=University of Southern Queensland&rft.description=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.&rft.creator=Dr Rajib Rana&rft.date=2021&rft.coverage=Queensland, Australia&rft_rights=NoLicence&rft_subject=Information Systems not elsewhere classified&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft_subject=INFORMATION SYSTEMS&rft.type=dataset&rft.language=English Access the data

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Postal Address:
University of Southern Queensland/ Springfield Campus, PO Box 4196, Springfield Central Qld 4300, Australia



Brief description

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.

Available: 29 04 2021

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152.90377,-27.68117 152.91008,-27.68117 152.91008,-27.68558 152.90377,-27.68558 152.90377,-27.68117

152.906921,-27.683376

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