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dc.contributor.authorChen, T
dc.contributor.authorKeravnou-Papailiou, E
dc.contributor.authorAntoniou, G
dc.description.abstractRecent advances in information technology have facilitated the massive collection of big data in numerous areas, including the healthcare sector. Healthcare data exists in various forms that can be briefly grouped into two categories. First, there is clinical data that is directly related to patients and medical conditions. This data includes, but is not limited to, demographic data, patient history, lab test results, physical examinations, diagnostic analysis and medical notes. The proliferation of wearable devices also enables the collection of clinical data periodically through monitoring systems via wireless technology, which supports the real-time tracking of patient care and timely adjustment of treatment plans. A second category of healthcare data originates from the business side of healthcare, such as operational and equipment costs, and logistic and administrative data which may be utilised for the optimisation of operational dynamics to support effective healthcare services and generally enhance utility in medical practice.
dc.publisherElsevier BV
dc.relation.ispartofjournalArtificial Intelligence in Medicine
dc.subject.fieldofresearchInformation and computing sciences
dc.titleMedical analytics for healthcare intelligence – Recent advances and future directions
dc.typeJournal article
dc.type.descriptionC2 - Articles (Other)
dcterms.bibliographicCitationChen, T; Keravnou-Papailiou, E; Antoniou, G, Medical analytics for healthcare intelligence – Recent advances and future directions, Artificial Intelligence in Medicine, 2021, 112, pp. 102009
gro.hasfulltextNo Full Text
gro.griffith.authorAntoniou, Grigorios

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