Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/19406
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dc.contributor.authorHandelman, G S-
dc.contributor.authorKok, H K-
dc.contributor.authorChandra, R V-
dc.contributor.authorRazavi, A H-
dc.contributor.authorLee, M J-
dc.contributor.authorAsadi, Hamed-
dc.date2018-08-13-
dc.date.accessioned2018-09-17T01:47:05Z-
dc.date.available2018-09-17T01:47:05Z-
dc.date.issued2018-08-13-
dc.identifier.citationJournal of internal medicine 2018; 284(6): 603-619-
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/19406-
dc.description.abstractMachine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer-aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine.-
dc.language.isoeng-
dc.subjectartificial intelligence-
dc.subjectmachine learning-
dc.subjectmedicine-
dc.subjectsupervised machine learning-
dc.subjectunsupervised machine learning-
dc.titleeDoctor: machine learning and the future of medicine.-
dc.typeJournal Article-
dc.identifier.journaltitleJournal of internal medicine-
dc.identifier.affiliationSchool of Medicine, Faculty of Health, Deakin University, Waurn Ponds, Victoria, Australiaen
dc.identifier.affiliationBCE Corporate Security, Ottawa, ON, Canadaen
dc.identifier.affiliationDepartment of Radiology, Beaumont Hospital and Royal College of Surgeons in Ireland, Dublin, Irelanden
dc.identifier.affiliationInterventional Neuroradiology Service, Department of Radiology, Austin Health, Heidelberg, Victoria, Australiaen
dc.identifier.affiliationRoyal Victoria Hospital, Belfast, UKen
dc.identifier.affiliationInterventional Radiology Service, Northern Hospital Radiology, Epping, Victoria, Australiaen
dc.identifier.affiliationInterventional Neuroradiology Service, Monash Imaging, Monash Health, Clayton, Victoria, Australiaen
dc.identifier.affiliationFaculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australiaen
dc.identifier.affiliationSchool of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canadaen
dc.identifier.doi10.1111/joim.12822-
dc.identifier.orcid0000-0003-4275-783Xen
dc.identifier.orcid0000-0003-2475-9727en
dc.identifier.pubmedid30102808-
dc.type.austinJournal Article-
dc.type.austinReview-
local.name.researcherAsadi, Hamed
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.languageiso639-1en-
item.openairetypeJournal Article-
item.cerifentitytypePublications-
crisitem.author.deptRadiology-
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