Article
Toward Digital Phenotypes of Early Childhood Mental Health via Unsupervised and Supervised Machine Learning.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference - 1 Jul 2023
Loftness Bryn C, Rizzo Donna M, Halvorson-Phelan Julia, O'Leary Aisling, Prytherch Shania, Bradshaw Carter, Brown Anna Jane, Cheney Nick, McGinnis Ellen W, McGinnis Ryan S
Abstract excerpt
Childhood mental health disorders such as anxiety, depression, and ADHD are commonly-occurring and often go undetected into adolescence or adulthood. This can lead to detrimental impacts on long-term wellbeing and quality of life. Current parent-report assessments for pre-school aged children are often biased, and thus increase the need for objective mental health screening tools. Leveraging digital tools to...
Topics
- Child
- Adolescent
- Humans
- Child, Preschool
- Adult
- Mental Health
- Quality of Life
- Anxiety
- Supervised Machine Learning
- Phenotype