Cargando…
ACCU(3)RATE: A mobile health application rating scale based on user reviews
BACKGROUND: Over the last decade, mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being. OBJECTIVE: This paper presents an Artificial Intelligence (AI)-enabled mHealth app rating tool, called ACCU(3)RATE, which takes multidimensional meas...
Autores principales: | Biswas, Milon, Tania, Marzia Hoque, Kaiser, M. Shamim, Kabir, Russell, Mahmud, Mufti, Kemal, Atika Ahmad |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675707/ https://www.ncbi.nlm.nih.gov/pubmed/34914718 http://dx.doi.org/10.1371/journal.pone.0258050 |
Ejemplares similares
-
Turkish Validation of the User Version of the Mobile Application Rating Scale
por: Calik, Gokhan, et al.
Publicado: (2022) -
Development and Validation of the User Version of the Mobile Application Rating Scale (uMARS)
por: Stoyanov, Stoyan R, et al.
Publicado: (2016) -
Greek validation of the user version of the Mobile Application Rating
Scale (uMARS)
por: Chasiotis, Georgios, et al.
Publicado: (2023) -
A machine learning pipeline to classify foetal heart rate deceleration with optimal feature set
por: Das, Sahana, et al.
Publicado: (2023) -
Response to “Development and Validation of the User Version of the Mobile Application Rating Scale (uMARS)”
por: Baptista, Shaira, et al.
Publicado: (2017)