Kavli Affiliate: Wei Gao
| First 5 Authors: Sijie Ji, Xinzhe Zheng, Jiawei Sun, Renqi Chen, Wei Gao
| Summary:
Mental health disorders are among the most prevalent diseases worldwide,
affecting nearly one in four people. Despite their widespread impact, the
intervention rate remains below 25%, largely due to the significant cooperation
required from patients for both diagnosis and intervention. The core issue
behind this low treatment rate is stigma, which discourages over half of those
affected from seeking help. This paper presents MindGuard, an accessible,
stigma-free, and professional mobile mental healthcare system designed to
provide mental health first aid. The heart of MindGuard is an innovative edge
LLM, equipped with professional mental health knowledge, that seamlessly
integrates objective mobile sensor data with subjective Ecological Momentary
Assessment records to deliver personalized screening and intervention
conversations. We conduct a broad evaluation of MindGuard using open datasets
spanning four years and real-world deployment across various mobile devices
involving 20 subjects for two weeks. Remarkably, MindGuard achieves results
comparable to GPT-4 and outperforms its counterpart with more than 10 times the
model size. We believe that MindGuard paves the way for mobile LLM
applications, potentially revolutionizing mental healthcare practices by
substituting self-reporting and intervention conversations with passive,
integrated monitoring within daily life, thus ensuring accessible and
stigma-free mental health support.
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