- Tytuł:
- Semi-supervised vs. supervised learning for mental health monitoring: A case study on bipolar disorder
- Autorzy:
-
Casalino, Gabriella
Castellano, Giovanna
Hryniewicz, Olgierd
Leite, Daniel
Opara, Karol
Radziszewska, Weronika
Kaczmarek-Majer, Katarzyna - Powiązania:
- https://bibliotekanauki.pl/articles/24200683.pdf
- Data publikacji:
- 2023
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
semi supervised learning
mental health monitoring
acoustic features
pattern recognition
AI in medicine
uczenie częściowo nadzorowane
monitorowanie zdrowia psychicznego
cechy akustyczne
rozpoznawanie wzorca - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2023, 33, 3; 419--428
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł