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Paper details
Number 3 - September 2023
Volume 33 - 2023
Semi-supervised vs. supervised learning for mental health monitoring: A case study on bipolar disorder
Gabriella Casalino, Giovanna Castellano, Olgierd Hryniewicz, Daniel Leite, Karol Opara, Weronika Radziszewska, Katarzyna Kaczmarek-Majer
Abstract
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. The majority of the related work on mental
health monitoring limits the considerations only to labeled data using a predefined ground-truth period. On the other hand,
semi-supervised methods make it possible to utilize the entire dataset, exploiting the regularities in the unlabeled portion of
the data to improve the predictive power of a model. To assess the applicability of semi-supervised learning approaches, we
discuss selected state-of-the-art semi-supervised classifiers, namely, label spreading, label propagation, a semi-supervised
support vector machine, and the self training classifier. We use real-world data obtained from a bipolar disorder patient to
compare the performance of the different methods with that of baseline supervised learning methods. The experiment shows
that semi-supervised learning algorithms can outperform supervised algorithms in predicting bipolar disorder episodes.
Keywords
semi-supervised learning, mental health monitoring, acoustic features, pattern recognition, AI in medicine