Deep Neural Networks on Mobile Healthcare Applications: Practical Recommendations

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dc.catalogadorjca
dc.contributor.authorBenedetto, Jose I.
dc.contributor.authorSanabria, Pablo
dc.contributor.authorNeyem, Andres
dc.contributor.authorNavon, Jaime
dc.contributor.authorPoellabauer, Christian
dc.contributor.authorXia, Bryan (Ning)
dc.date.accessioned2024-03-27T13:32:08Z
dc.date.available2024-03-27T13:32:08Z
dc.date.issued2018
dc.description.abstractDeep learning has for a long time been recognized as a powerful tool in the field of medicine for making predictions or detecting abnormalities in a patient’s data. However, up until recently, hosting of these neural networks has been relegated to the domain of servers and powerful workstations due to the vast amount of resources they require. This trend has been steadily shifting in the recent years, and we are now beginning to see more and more mobile applications with similar capabilities. Deep neural networks hosted completely on mobile platforms are extremely valuable for providing healthcare services to remote areas without network connectivity. Yet despite this, there is very little information regarding the migration process of an existing server-based neural network to a mobile environment. In this work, we describe the various techniques and considerations that should be taken into account when developing a deep-learning enabled mobile application with offline support. We illustrate the above by providing a concrete example through our experience in migrating to mobile an in-house developed medical application for detecting early signs of traumatic brain injuries.
dc.fuente.origenORCID
dc.identifier.doi10.3390/proceedings2190550
dc.identifier.issn2504-3900
dc.identifier.urihttps://doi.org/10.3390/proceedings2190550
dc.identifier.urihttp://www.mdpi.com/2504-3900/2/19/550
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/84800
dc.information.autorucActividades Universitarias-Dri; Sanabria Quispe, Pablo; 0000-0001-6493-3895; 212656
dc.issue.numero19
dc.language.isoen
dc.nota.accesocontenido completo
dc.pagina.final12
dc.pagina.inicio1
dc.relation.ispartof2th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2018), Punta Cana, Dominican Republic, 4–7 December 2018
dc.revistaProceedings
dc.rightsacceso abierto
dc.subjectMachine learning
dc.subjectMobile devices
dc.subjectMobile healthcare
dc.subjectDeep learning
dc.subjectKeras
dc.subjectTensorflow
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.titleDeep Neural Networks on Mobile Healthcare Applications: Practical Recommendations
dc.typeartículo
dc.volumen2
sipa.codpersvinculados212656
sipa.trazabilidadORCID;2024-03-25
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