Semi-supervised vs. supervised learning for mental health monitoring: A case study on bipolar disorder - Casalino, Gabriella; Castellano, Giovanna; Hryniewicz, Olgierd (1948- ); Leite, Daniel; Opara, Karol; Radziszewska, Weronika; Kaczmarek-Majer, Katarzyna (Zielona Góra: Uniwersytet Zielonogórski)
Acoustic features of speech are promising as objective markers for mental health monitoring. Specialized smartphone apps can gather such acoustic data without disrupting the daily activities of patients. Nonetheless, the psychiatric assessment of the patient`s mental state is typically a sporadic occurrence that takes place every few months. Consequently, only a slight fraction of the acoustic data is labeled and applicable for supervised learning.
Keywords: semi-supervised learning; mental health monitoring; acoustic features; pattern recognition; AI in medicine