Please use this identifier to cite or link to this item: http://hdl.handle.net/11054/1780
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dc.contributorChua, S. J.en_US
dc.contributorWrigley, Scotten_US
dc.contributorHair, Caseyen_US
dc.contributorSahathevan, Rameshen_US
dc.date.accessioned2021-10-04T02:07:09Z-
dc.date.available2021-10-04T02:07:09Z-
dc.date.issued2021-
dc.identifier.govdoc01734en_US
dc.identifier.urihttp://hdl.handle.net/11054/1780-
dc.description.abstractDelirium remains a significant cause of morbidity, mortality and economic burden to society. "Big data" refers to data of significantly large volume, obtained from a variety of resources, which is created and processed at high velocity. We conducted a systematic review and meta-analysis exploring whether big data could predict the incidence of delirium of patients in the inpatient setting. Medline, Embase, the Cochrane Library, Web of Science, CINAHL, clinicaltrials.gov, who.int and IEEE Xplore were searched using MeSH terms "big data", "data mining", "delirium" and "confusion" up to 30th September 2019. We included both randomised and observational studies. The primary outcome of interest was development of delirium and the secondary outcomes of interest were type of statistical methods used, variables included in the mining algorithms and clinically important outcomes such as mortality and length of hospital stay. The quality of studies was graded using the CHARMs checklist. Six retrospective single centre observational studies were included (n = 178,091), of which 17, 574 participants developed delirium. Studies were of generally of low to moderate quality. The most commonly studied method was random forest, followed by support vector machine and artificial neural networks. The model with best performance for delirium prediction was random forest, with area under receiver operating curve (AUROC) ranging from 0.78 to 0.91. Sensitivity ranged from 0.59 to 0.81 and specificity ranged from 0.73 to 0.92. Our systematic review suggests that machine-learning techniques can be utilised to predict delirium.en_US
dc.description.provenanceSubmitted by Gemma Siemensma (gemmas@bhs.org.au) on 2021-08-17T01:40:14Z No. of bitstreams: 0en
dc.description.provenanceApproved for entry into archive by Gemma Siemensma (gemmas@bhs.org.au) on 2021-10-04T02:07:09Z (GMT) No. of bitstreams: 0en
dc.description.provenanceMade available in DSpace on 2021-10-04T02:07:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2021en
dc.titlePrediction of delirium using data mining: a systematic review.en_US
dc.typeJournal Articleen_US
dc.type.specifiedArticleen_US
dc.bibliographicCitation.titleJournal of Clinial Neuroscienceen_US
dc.bibliographicCitation.volume91en_US
dc.bibliographicCitation.stpage288en_US
dc.bibliographicCitation.endpage298en_US
dc.subject.healththesaurusASSOCIATION MININGen_US
dc.subject.healththesaurusBIG DATAen_US
dc.subject.healththesaurusDATA MININGen_US
dc.subject.healththesaurusMACHINE LEARNINGen_US
dc.subject.healththesaurusDELIRIUMen_US
dc.subject.healththesaurusSYSTEMATIC REVIEWen_US
dc.identifier.doihttps://doi.org/10.1016/j.jocn.2021.07.029en_US
Appears in Collections:Research Output

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