Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0193259
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dc.titleA dual boundary classifier for predicting acute hypotensive episodes in critical care
dc.contributor.authorBhattacharya S.
dc.contributor.authorHuddar V.
dc.contributor.authorRajan V.
dc.contributor.authorReddy C.K.
dc.date.accessioned2019-11-01T08:16:50Z
dc.date.available2019-11-01T08:16:50Z
dc.date.issued2018
dc.identifier.citationBhattacharya S., Huddar V., Rajan V., Reddy C.K. (2018). A dual boundary classifier for predicting acute hypotensive episodes in critical care. PLoS ONE 13 (2) : e0193259. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0193259
dc.identifier.issn19326203
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/161236
dc.description.abstractAn Acute Hypotensive Episode (AHE) is the sudden onset of a sustained period of low blood pressure and is one among the most critical conditions in Intensive Care Units (ICU). Without timely medical care, it can lead to an irreversible organ damage and death. By identifying patients at risk for AHE early, adequate medical intervention can save lives and improve patient outcomes. In this paper, we design a novel dual–boundary classification based approach for identifying patients at risk for AHE. Our algorithm uses only simple summary statistics of past Blood Pressure measurements and can be used in an online environment facilitating real–time updates and prediction. We perform extensive experiments with more than 4,500 patient records and demonstrate that our method outperforms the previous best approaches of AHE prediction. Our method can identify AHE patients two hours in advance of the onset, giving sufficient time for appropriate clinical intervention with nearly 80% sensitivity and at 95% specificity, thus having very few false positives. © 2018 Bhattacharya et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20191101
dc.subjectadverse outcome
dc.subjectalgorithm
dc.subjectArticle
dc.subjectblood pressure measurement
dc.subjectclassifier
dc.subjectdata processing
dc.subjectdeath
dc.subjectdiagnostic accuracy
dc.subjectfalse positive result
dc.subjecthuman
dc.subjecthypotension
dc.subjectintensive care
dc.subjectintensive care unit
dc.subjectmedical care
dc.subjectmedical record
dc.subjectonline system
dc.subjectorgan injury
dc.subjectpatient coding
dc.subjectprediction
dc.subjectrisk assessment
dc.subjectrisk factor
dc.subjectrisk management
dc.subjectsensitivity and specificity
dc.subjectstatistical analysis
dc.subjectbiological model
dc.subjectblood pressure
dc.subjectelectronic medical record system
dc.subjectfemale
dc.subjectintensive care
dc.subjectmale
dc.subjectpathophysiology
dc.subjectpredictive value
dc.subjectprocedures
dc.subjectBlood Pressure
dc.subjectCritical Care
dc.subjectFemale
dc.subjectHumans
dc.subjectHypotension
dc.subjectMale
dc.subjectMedical Records Systems, Computerized
dc.subjectModels, Cardiovascular
dc.subjectPredictive Value of Tests
dc.typeArticle
dc.contributor.departmentDEPARTMENT OF INFORMATION SYSTEMS AND ANALYTICS
dc.description.doi10.1371/journal.pone.0193259
dc.description.sourcetitlePLoS ONE
dc.description.volume13
dc.description.issue2
dc.description.pagee0193259
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This item is licensed under a Creative Commons License Creative Commons