FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2920868987> ?p ?o ?g. }

• W2920868987 endingPage "129" @default.
• W2920868987 startingPage "113" @default.
• W2920868987 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." @default.
• W2920868987 created "2019-03-22" @default.
• W2920868987 creator A5073537068 @default.
• W2920868987 creator A5074330876 @default.
• W2920868987 creator A5084010930 @default.
• W2920868987 date "2020-01-01" @default.
• W2920868987 modified "2023-10-10" @default.
• W2920868987 title "Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities, and Challenges" @default.
• W2920868987 cites W107445507 @default.
• W2920868987 cites W1520242404 @default.
• W2920868987 cites W1827530491 @default.
• W2920868987 cites W1858759550 @default.
• W2920868987 cites W1948745668 @default.
• W2920868987 cites W1965775572 @default.
• W2920868987 cites W1969959732 @default.
• W2920868987 cites W1974455934 @default.
• W2920868987 cites W1976981274 @default.
• W2920868987 cites W1979062697 @default.
• W2920868987 cites W1980946892 @default.
• W2920868987 cites W1982779742 @default.
• W2920868987 cites W1987011701 @default.
• W2920868987 cites W1990638468 @default.
• W2920868987 cites W1991258631 @default.
• W2920868987 cites W1997038958 @default.
• W2920868987 cites W2004421347 @default.
• W2920868987 cites W2012651380 @default.
• W2920868987 cites W2012653385 @default.
• W2920868987 cites W2015143976 @default.
• W2920868987 cites W2016740629 @default.
• W2920868987 cites W2024063375 @default.
• W2920868987 cites W2025786233 @default.
• W2920868987 cites W2028140375 @default.
• W2920868987 cites W2031655994 @default.
• W2920868987 cites W2033358375 @default.
• W2920868987 cites W2042070546 @default.
• W2920868987 cites W2043549555 @default.
• W2920868987 cites W2049295438 @default.
• W2920868987 cites W2050655099 @default.
• W2920868987 cites W2053675728 @default.
• W2920868987 cites W2053836765 @default.
• W2920868987 cites W2054794722 @default.
• W2920868987 cites W2057134078 @default.
• W2920868987 cites W2058227548 @default.
• W2920868987 cites W2060363472 @default.
• W2920868987 cites W2061297199 @default.
• W2920868987 cites W2067140282 @default.
• W2920868987 cites W2074331093 @default.
• W2920868987 cites W2079484785 @default.
• W2920868987 cites W2093103229 @default.
• W2920868987 cites W2093602450 @default.
• W2920868987 cites W2094637188 @default.
• W2920868987 cites W2096081771 @default.
• W2920868987 cites W2105037799 @default.
• W2920868987 cites W2107438685 @default.
• W2920868987 cites W2109140840 @default.
• W2920868987 cites W2111564049 @default.
• W2920868987 cites W2115017507 @default.
• W2920868987 cites W2117539524 @default.
• W2920868987 cites W2119406369 @default.
• W2920868987 cites W2119619008 @default.
• W2920868987 cites W2120111102 @default.
• W2920868987 cites W2121282288 @default.
• W2920868987 cites W2129259959 @default.
• W2920868987 cites W2138973641 @default.
• W2920868987 cites W2146087900 @default.
• W2920868987 cites W2149986403 @default.
• W2920868987 cites W2154758450 @default.
• W2920868987 cites W2161341729 @default.
• W2920868987 cites W2161381512 @default.
• W2920868987 cites W2163922914 @default.
• W2920868987 cites W2163980871 @default.
• W2920868987 cites W2171831801 @default.
• W2920868987 cites W2192572088 @default.
• W2920868987 cites W2252171711 @default.
• W2920868987 cites W2257979135 @default.
• W2920868987 cites W2282821441 @default.
• W2920868987 cites W2321227683 @default.
• W2920868987 cites W2343557095 @default.
• W2920868987 cites W2550821151 @default.
• W2920868987 cites W2583500168 @default.
• W2920868987 cites W2592023181 @default.
• W2920868987 cites W2683833049 @default.
• W2920868987 cites W2713640100 @default.
• W2920868987 cites W2735045133 @default.
• W2920868987 cites W2797883881 @default.
• W2920868987 cites W2951934944 @default.
• W2920868987 cites W2952239877 @default.
• W2920868987 cites W2962914239 @default.
• W2920868987 cites W2963059730 @default.
• W2920868987 cites W4240769272 @default.
• W2920868987 cites W4301058135 @default.
• W2920868987 doi "https://doi.org/10.1109/rbme.2019.2904488" @default.
• W2920868987 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30872241" @default.
• W2920868987 hasPublicationYear "2020" @default.
• W2920868987 type Work @default.
• W2920868987 sameAs 2920868987 @default.
• W2920868987 citedByCount "31" @default.
• W2920868987 countsByYear W29208689872020 @default.

• next