Please use this identifier to cite or link to this item: 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

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1371_journal_pone_0193259.pdf1.47 MBAdobe PDF

OPEN

NoneView/Download

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons