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dc.contributor.authorTsang, Gen
dc.contributor.authorXie, Xen
dc.contributor.authorZhou, S-Men
dc.date.accessioned2021-11-05T11:53:11Z
dc.date.available2021-11-05T11:53:11Z
dc.date.issued2020en
dc.identifier.urihttp://hdl.handle.net/10026.1/18231
dc.description.abstract

Dementia is a chronic and degenerative condition affecting millions globally. The care of patients with dementia presents an ever-continuing challenge to healthcare systems in the 21st century. Medical and health sciences have generated unprecedented volumes of data related to health and wellbeing for patients with dementia due to advances in information technology, such as genetics, neuroimaging, cognitive assessment, free texts, routine electronic health records, etc. Making the best use of these diverse and strategic resources will lead to high-quality care of patients with dementia. As such, machine learning becomes a crucial factor in achieving this objective. The aim of this paper is to provide a state-of-the-art review of machine learning methods applied to health informatics for dementia care. We collate and review the existing scientific methodologies and identify the relevant issues and challenges when faced with big health data. Machine learning has demonstrated promising applications to neuroimaging data analysis for dementia care, while relatively less effort has been made to make use of integrated heterogeneous data via advanced machine learning approaches. We further indicate future potential and research directions in applying advanced machine learning, such as deep learning, to dementia informatics.

en
dc.format.extent113 - 129en
dc.languageengen
dc.language.isoengen
dc.subjectBiomedical Researchen
dc.subjectDementiaen
dc.subjectHumansen
dc.subjectMachine Learningen
dc.subjectMedical Informaticsen
dc.subjectMental Status and Dementia Testsen
dc.subjectNatural Language Processingen
dc.subjectNeuroimagingen
dc.titleHarnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities, and Challenges.en
dc.typeJournal Article
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/30872241en
plymouth.volume13en
plymouth.publication-statusPublisheden
plymouth.journalIEEE Rev Biomed Engen
dc.identifier.doi10.1109/RBME.2019.2904488en
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Health
plymouth.organisational-group/Plymouth/Faculty of Health/School of Nursing and Midwifery
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA03 Allied Health Professions, Dentistry, Nursing and Pharmacy
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dc.publisher.placeUnited Statesen
dc.identifier.eissn1941-1189en
dc.rights.embargoperiodNot knownen
rioxxterms.versionofrecord10.1109/RBME.2019.2904488en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.typeJournal Article/Reviewen


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