Please use this identifier to cite or link to this item:
https://doi.org/10.1371/journal.pone.0193259
DC Field | Value | |
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dc.title | A dual boundary classifier for predicting acute hypotensive episodes in critical care | |
dc.contributor.author | Bhattacharya S. | |
dc.contributor.author | Huddar V. | |
dc.contributor.author | Rajan V. | |
dc.contributor.author | Reddy C.K. | |
dc.date.accessioned | 2019-11-01T08:16:50Z | |
dc.date.available | 2019-11-01T08:16:50Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Bhattacharya 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.issn | 19326203 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/161236 | |
dc.description.abstract | An 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.rights | Attribution 4.0 International | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.source | Unpaywall 20191101 | |
dc.subject | adverse outcome | |
dc.subject | algorithm | |
dc.subject | Article | |
dc.subject | blood pressure measurement | |
dc.subject | classifier | |
dc.subject | data processing | |
dc.subject | death | |
dc.subject | diagnostic accuracy | |
dc.subject | false positive result | |
dc.subject | human | |
dc.subject | hypotension | |
dc.subject | intensive care | |
dc.subject | intensive care unit | |
dc.subject | medical care | |
dc.subject | medical record | |
dc.subject | online system | |
dc.subject | organ injury | |
dc.subject | patient coding | |
dc.subject | prediction | |
dc.subject | risk assessment | |
dc.subject | risk factor | |
dc.subject | risk management | |
dc.subject | sensitivity and specificity | |
dc.subject | statistical analysis | |
dc.subject | biological model | |
dc.subject | blood pressure | |
dc.subject | electronic medical record system | |
dc.subject | female | |
dc.subject | intensive care | |
dc.subject | male | |
dc.subject | pathophysiology | |
dc.subject | predictive value | |
dc.subject | procedures | |
dc.subject | Blood Pressure | |
dc.subject | Critical Care | |
dc.subject | Female | |
dc.subject | Humans | |
dc.subject | Hypotension | |
dc.subject | Male | |
dc.subject | Medical Records Systems, Computerized | |
dc.subject | Models, Cardiovascular | |
dc.subject | Predictive Value of Tests | |
dc.type | Article | |
dc.contributor.department | DEPARTMENT OF INFORMATION SYSTEMS AND ANALYTICS | |
dc.description.doi | 10.1371/journal.pone.0193259 | |
dc.description.sourcetitle | PLoS ONE | |
dc.description.volume | 13 | |
dc.description.issue | 2 | |
dc.description.page | e0193259 | |
Appears in Collections: | Staff Publications Elements |
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