Please use this identifier to cite or link to this item:
DC FieldValue
dc.titlePrediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: A big data-driven, machine learning approach
dc.contributor.authorDu, H
dc.contributor.authorSiah, KTH
dc.contributor.authorRu-Yan, VZ
dc.contributor.authorTeh, R
dc.contributor.authorEn Tan, CY
dc.contributor.authorYeung, W
dc.contributor.authorScaduto, C
dc.contributor.authorBolongaita, S
dc.contributor.authorCruz, MTK
dc.contributor.authorLiu, M
dc.contributor.authorLin, X
dc.contributor.authorTan, YY
dc.contributor.authorFeng, M
dc.identifier.citationDu, H, Siah, KTH, Ru-Yan, VZ, Teh, R, En Tan, CY, Yeung, W, Scaduto, C, Bolongaita, S, Cruz, MTK, Liu, M, Lin, X, Tan, YY, Feng, M (2021-11-17). Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: A big data-driven, machine learning approach. BMJ Open Gastroenterology 8 (1) : e000761-. ScholarBank@NUS Repository.
dc.description.abstractResearch objectives Clostriodiodes difficile infection (CDI) is a major cause of healthcare-associated diarrhoea with high mortality. There is a lack of validated predictors for severe outcomes in CDI. The aim of this study is to derive and validate a clinical prediction tool for CDI in-hospital mortality using a large critical care database. Methodology The demographics, clinical parameters, laboratory results and mortality of CDI were extracted from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. We subsequently trained three machine learning models: logistic regression (LR), random forest (RF) and gradient boosting machine (GBM) to predict in-hospital mortality. The individual performances of the models were compared against current severity scores (Clostridiodes difficile Associated Risk of Death Score (CARDS) and ATLAS (Age, Treatment with systemic antibiotics, leukocyte count, Albumin and Serum creatinine as a measure of renal function) by calculating area under receiver operating curve (AUROC). We identified factors associated with higher mortality risk in each model. Summary of results From 61 532 intensive care unit stays in the MIMIC-III database, there were 1315 CDI cases. The mortality rate for CDI in the study cohort was 18.33%. AUROC was 0.69 (95% CI, 0.60 to 0.76) for LR, 0.71 (95% CI, 0.62 to 0.77) for RF and 0.72 (95% CI, 0.64 to 0.78) for GBM, while previously AUROC was 0.57 (95% CI, 0.51 to 0.65) for CARDS and 0.63 (95% CI, 0.54 to 0.70) for ATLAS. Albumin, lactate and bicarbonate were significant mortality factors for all the models. Free calcium, potassium, white blood cell, urea, platelet and mean blood pressure were present in at least two of the three models. Conclusion Our machine learning derived CDI in-hospital mortality prediction model identified pertinent factors that can assist critical care clinicians in identifying patients at high risk of dying from CDI.
dc.subjectbacterial infection
dc.subjectdietary - gastrointestinal infections
dc.subjectBig Data
dc.subjectCritical Care
dc.subjectHospital Mortality
dc.subjectMachine Learning
dc.contributor.departmentDEPT OF MEDICINE
dc.description.sourcetitleBMJ Open Gastroenterology
Appears in Collections:Elements

Show simple item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database a big data-driven, ma.pdf823.9 kBAdobe PDF



Page view(s)

checked on Nov 17, 2022


checked on Nov 17, 2022

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.