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<p>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.</p>
</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.

Semi–Supervised vs. Supervised Learning for Mental Health Monitoring: A Case Study on Bipolar Disorder

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Acoustic features of speech are promising as objective markers for mental health monitoring. Specialized smartphone apps can gather such acoustic data without...

Semi–Supervised vs. Supervised Learning for Mental Health Monitoring: A Case Study on Bipolar Disorder

Published Online: Sep 21, 2023

Page range: 419 - 428

Received: Nov 21, 2022

Accepted: May 04, 2023

DOI: https://doi.org/10.34768/amcs-2023-0030

Keywords
semi-supervised learning, mental health monitoring, acoustic features, pattern recognition, AI in medicine

© 2023 Gabriella Casalino et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Semi–Supervised vs. Supervised Learning for Mental Health Monitoring: A Case Study on Bipolar Disorder

Published Online: Sep 21, 2023

Page range: 419 - 428

Received: Nov 21, 2022

Accepted: May 04, 2023

DOI: https://doi.org/10.34768/amcs-2023-0030

Keywordssemi-supervised learning, mental health monitoring, acoustic features, pattern recognition, AI in medicine

© 2023 Gabriella Casalino et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
semi-supervised learning, mental health monitoring, acoustic features, pattern recognition, AI in medicine