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https://doi.org/10.1371/journal.pone.0193259
Title: | A dual boundary classifier for predicting acute hypotensive episodes in critical care | Authors: | Bhattacharya S. Huddar V. Rajan V. Reddy C.K. |
Keywords: | adverse outcome algorithm Article blood pressure measurement classifier data processing death diagnostic accuracy false positive result human hypotension intensive care intensive care unit medical care medical record online system organ injury patient coding prediction risk assessment risk factor risk management sensitivity and specificity statistical analysis biological model blood pressure electronic medical record system female intensive care male pathophysiology predictive value procedures Blood Pressure Critical Care Female Humans Hypotension Male Medical Records Systems, Computerized Models, Cardiovascular Predictive Value of Tests |
Issue Date: | 2018 | 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 | Rights: | Attribution 4.0 International | 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. | Source Title: | PLoS ONE | URI: | https://scholarbank.nus.edu.sg/handle/10635/161236 | ISSN: | 19326203 | DOI: | 10.1371/journal.pone.0193259 | Rights: | Attribution 4.0 International |
Appears in Collections: | Staff Publications Elements |
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