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Toward an explainable public health intelligence to detect depression using mobile application usage
Cita
Sabzizadeh, Ehsan, Rello Sánchez, Luz and Urueña López, Alberto 
ORCID: https://orcid.org/0000-0001-8866-882X
(2024).
Toward an explainable public health intelligence to detect
depression using mobile application usage.
En: "16th Mediterranean Conference on Information Systems (MCIS)", 3 al 5 de octubre de 2024, Oporto, Portugal. ISBN 978-989-33-6886-2.
ORCID: https://orcid.org/0000-0001-8866-882X
(2024).
Toward an explainable public health intelligence to detect
depression using mobile application usage.
En: "16th Mediterranean Conference on Information Systems (MCIS)", 3 al 5 de octubre de 2024, Oporto, Portugal. ISBN 978-989-33-6886-2.
Descripción
| Título: | Toward an explainable public health intelligence to detect depression using mobile application usage |
|---|---|
| Autor/es: |
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| Tipo de Documento: | Ponencia en Congreso o Jornada (Artículo) |
| Título del Evento: | 16th Mediterranean Conference on Information Systems (MCIS) |
| Fechas del Evento: | 3 al 5 de octubre de 2024 |
| Lugar del Evento: | Oporto, Portugal |
| Título del Libro: | 16th Mediterranean Conference on Information Systems (MCIS) 2024 Proceedings |
| Fecha: | 10 Marzo 2024 |
| ISBN: | 978-989-33-6886-2 |
| Materias: | |
| ODS: | |
| Escuela: | E.T.S.I. Industriales (UPM) |
| Departamento: | Ingeniería de Organización, Administración de Empresas y Estadística |
| Licencias Creative Commons: | Ninguna |
Resumen
The purpose of this research is to design a public mental health intelligence based on explainable ma-chine learning. Historical data on user application usage behavior for four subsequent semesters from the Cybersecurity and Confidence in the Spanish Households National Survey were used, and an IT artifact was developed to detect depression. Historical use of mobile applications can partially predict user depression symptoms, and when we add sociodemographic data (gender, educational level, and age), our model performance reaches acceptable results. Finally, we implement post-hoc explainable algorithms at local and global levels, providing us with a detailed analysis of the variables that derive depression.
Más información
| ID de Registro: | 91965 |
|---|---|
| Identificador DC: | https://oa.upm.es/91965/ |
| Identificador OAI: | oai:oa.upm.es:91965 |
| URL Oficial: | https://aisel.aisnet.org/mcis2024/47/ |
| Depositado por: | Alberto Urueña |
| Depositado el: | 21 Nov 2025 07:53 |
| Ultima Modificación: | 21 Nov 2025 07:53 |
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