Detecting Depression Using a Framework Combining Deep Multimodal Neural Networks with a Purpose-Built Automated Evaluation

From WebRef.org
Jump to navigationJump to search Nexgard-For-Dogs-all-size-pack

Machine learning (ML) has been introduced into the medical field as a means to provide diagnostic tools capable of enhancing accuracy and precision while minimizing laborious tasks that require human intervention. There is mounting evidence that the technology fueled by ML has the potential to detect, and substantially improve treatment of complex mental disorders such as depression. We developed a framework capable of detecting depression with minimal human intervention: AiME (Artificial Intelligence Mental Evaluation). AiME consists of a short human-computer interactive evaluation and artificial intelligence, namely deep learning, and can predict whether the participant is depressed or not with satisfactory performance. Due to its ease of use, this technology can offer a viable tool for mental health professionals to identify symptoms of depression, thus enabling a faster preventative intervention. Furthermore, it may alleviate the challenge of interpreting highly nuanced physiological and behavioral biomarkers of depression by providing a more objective evaluation.

Victor, E., M. Aghajan, Z., Sewart, A. R., & Christian, R. (2018, September 30). Detecting Depression Using a Framework Combining Deep Multimodal Neural Networks with a Purpose-Built Automated Evaluation. https://doi.org/10.31234/osf.io/vqpcz


Sponsor: Get up to 80% Off Dealer Prices on Shocks Replacements! Shop Now at Parts Geek!