Decision support system for the differentiation of schizophrenia and mood disorders using multiple deep learning models on wearable devices data.
Health Informatics J
; 28(4): 14604582221137537, 2022.
Article
en En
| MEDLINE
| ID: mdl-36317536
In the modern world, with so much inherent stress, mental health disorders (MHDs) are becoming more common in every country around the globe, causing a significant burden on society and patients' families. MHDs come in many forms with various severities of symptoms and differing periods of suffering, and as a result it is difficult to differentiate between them and simple to confuse them with each other. Therefore, we propose a support system that employs deep learning (DL) with wearable device data to provide physicians with an objective reference resource by which to make differential diagnoses and plan treatment. We conducted experiments on open datasets containing activity motion signal data from wearable devices to identify schizophrenia and mood disorders (bipolar and unipolar), the datasets being named Psykose and Depresjon. The results showed that, in both workflow approaches, the proposed framework performed well in comparison with the traditional machine learning (ML) and DL methods. We concluded that applying DL models using activity motion signal data from wearable devices represents a prospective objective support system for MHD differentiation with a good performance.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Esquizofrenia
/
Dispositivos Electrónicos Vestibles
/
Aprendizaje Profundo
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Health Informatics J
Año:
2022
Tipo del documento:
Article
País de afiliación:
Taiwán
Pais de publicación:
Reino Unido