Repozytorium Uniwersytetu Zielonogórskiego udostępnia 9 312 obiektów cyfrowych
Obiekt
Tytuł: Semi-supervised vs. supervised learning for mental health monitoring: A case study on bipolar disorder
Creator:
Casalino, Gabriella ; Castellano, Giovanna ; Hryniewicz, Olgierd (1948- ) ; Leite, Daniel ; Opara, Karol ; Radziszewska, Weronika ; Kaczmarek-Majer, Katarzyna
Date:
Resource Type:
Contributor:
Foryś, Urszula - ed. ; Rejniak, Katarzyna - ed. ; Pękala, Barbara - ed. ; Bartłomiejczyk, Agnieszka - ed.
Subtitle:
Group publication title:
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.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Resource Identifier:
DOI:
Pages:
Source:
AMCS, volume 33, number 3 (2023) ; kliknij tutaj, żeby przejść
Language:
License CC BY 4.0:
Rights:
Biblioteka Uniwersytetu Zielonogórskiego
Kolekcje, do których przypisany jest obiekt:
- Repository > Faculties > Faculty of Computer, Electrical and Control Engineering
- Repository > Types of work > Articles
- Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2023)
Data ostatniej modyfikacji:
28 lip 2025
Data dodania obiektu:
28 lip 2025
Liczba wyświetleń treści obiektu:
9
Wszystkie dostępne wersje tego obiektu:
https://zbc.uz.zgora.pl/repozytorium/publication/101609
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 33, number 1 (2023)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 33, number 2 (2023)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 33, number 3 (2023)
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International Journal of Applied Mathematics and Computer Science (AMCS), volume 33, number 4 (2023)
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| Nazwa wydania | Data |
|---|---|
| Semi-supervised vs. supervised learning for mental health monitoring: A case study on bipolar disorder | 28 lip 2025 |
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