Please use this identifier to cite or link to this item: http://hdl.handle.net/11054/1780
Title: Prediction of delirium using data mining: a systematic review.
Author: Chua, S. J.
Wrigley, Scott
Hair, Casey
Sahathevan, Ramesh
Issue Date: 2021
Publication Title: Journal of Clinial Neuroscience
Volume: 91
Start Page: 288
End Page: 298
Abstract: Delirium 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.
URI: http://hdl.handle.net/11054/1780
DOI: https://doi.org/10.1016/j.jocn.2021.07.029
Internal ID Number: 01734
Health Subject: ASSOCIATION MINING
BIG DATA
DATA MINING
MACHINE LEARNING
DELIRIUM
SYSTEMATIC REVIEW
Type: Journal Article
Article
Appears in Collections:Research Output

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