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A novel classification method for predicting acute hypotensive episodes in critical care

Authors:

Sakyajit Bhattacharya,

Vijay HuddarAuthors Info & Claims

BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Pages 43 - 52

https://doi.org/10.1145/2649387.2649400

Published: 20 September 2014 Publication History

Abstract

An Acute Hypotensive Episode (AHE) is the sudden onset of a period of sustained low blood pressure and is one of the most critical conditions in Intensive Care Units (ICU). Without timely medical care, it can lead to irreversible organ damage and death. By identifying patients at risk for this complication, adequate medical intervention can save lives and improve patient outcomes.

In this paper we study the problem of identifying patients at risk for AHE. We cast the problem as a supervised classification task and design a novel dual--boundary classification algorithm. Our algorithm uses only past blood pressure measurements of the patients thereby being much simpler than many existing methods that use multiple sources of data. It can also be used in online or batch mode which is advantageous in an ICU setting. Our extensive experiments on 1700 patients' records demonstrate that the algorithm significantly outperforms existing approaches in predictive accuracy, sensitivity and specificity. It can identify patients at risk for AHE with nearly 95% accuracy up to 120 minutes before the episode begins.

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Cited By

Galeana-Zapién HAldana-Bobadilla EArciniegas-García JVargas-Gómez JVillalobos-Silva J(2024)A flexible framework for coding and predicting acute hypotensive episodes using Markov chainsKnowledge-Based Systems10.1016/j.knosys.2023.111237284:COnline publication date: 25-Jan-2024
https://dl.acm.org/doi/10.1016/j.knosys.2023.111237
Jha SFeng WWang MYoon B(2023)GRAPPEL: A Graph-based Approach for Early Risk Assessment of Acute Hypertension in Critical CareProceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/3584371.3612978(1-6)Online publication date: 3-Sep-2023
https://dl.acm.org/doi/10.1145/3584371.3612978
Chan BChen BSedghi ALaird PMaslove DMousavi P(2020)Generalizable deep temporal models for predicting episodes of sudden hypotension in critically ill patients: a personalized approachScientific Reports10.1038/s41598-020-67952-010:1Online publication date: 10-Jul-2020
https://doi.org/10.1038/s41598-020-67952-0
Show More Cited By

Index Terms

A novel classification method for predicting acute hypotensive episodes in critical care
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
    2. Health informatics
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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cover image ACM Conferences

BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

September 2014

851 pages

ISBN:9781450328944

DOI:10.1145/2649387

General Chairs:
Pierre Baldi
University of California, Irvine
,
Wei Wang
University of California, Los Angeles

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGBio: ACM Special Interest Group on Bioinformatics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2014

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BCB '14: ACM-BCB '14

September 20 - 23, 2014

California, Newport Beach

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Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Galeana-Zapién HAldana-Bobadilla EArciniegas-García JVargas-Gómez JVillalobos-Silva J(2024)A flexible framework for coding and predicting acute hypotensive episodes using Markov chainsKnowledge-Based Systems10.1016/j.knosys.2023.111237284:COnline publication date: 25-Jan-2024
https://dl.acm.org/doi/10.1016/j.knosys.2023.111237
Jha SFeng WWang MYoon B(2023)GRAPPEL: A Graph-based Approach for Early Risk Assessment of Acute Hypertension in Critical CareProceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/3584371.3612978(1-6)Online publication date: 3-Sep-2023
https://dl.acm.org/doi/10.1145/3584371.3612978
Chan BChen BSedghi ALaird PMaslove DMousavi P(2020)Generalizable deep temporal models for predicting episodes of sudden hypotension in critically ill patients: a personalized approachScientific Reports10.1038/s41598-020-67952-010:1Online publication date: 10-Jul-2020
https://doi.org/10.1038/s41598-020-67952-0
Chan BSedghi ALaird PMaslove DMousavi P(2019)Prediction of Patient-specific Acute Hypotensive Episodes in ICU Using Deep Models2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)10.1109/EMBC.2019.8856985(566-569)Online publication date: Jul-2019
https://doi.org/10.1109/EMBC.2019.8856985
Bhattacharya SHuddar VRajan VReddy C(2018)A dual boundary classifier for predicting acute hypotensive episodes in critical carePLOS ONE10.1371/journal.pone.019325913:2(e0193259)Online publication date: 23-Feb-2018
https://doi.org/10.1371/journal.pone.0193259
Patel ASingh IBrand LRao R(2017)A weighted similarity measure approach to predict intensive care unit transfers2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2017.8217806(1079-1084)Online publication date: Nov-2017
https://doi.org/10.1109/BIBM.2017.8217806
Fu LLi FZhou JWen XYao JShepherd M(2016)Event Prediction in Healthcare AnalyticsRevised Selected Papers of the PAKDD 2016 Workshops on Trends and Applications in Knowledge Discovery and Data Mining - Volume 979410.1007/978-3-319-42996-0_15(181-189)Online publication date: 19-Apr-2016
https://dl.acm.org/doi/10.1007/978-3-319-42996-0_15
Shrivastava HHuddar VBhattacharya SRajan V(2015)Classification with imbalanceProceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM.2015.7359773(707-714)Online publication date: 9-Nov-2015
https://dl.acm.org/doi/10.1109/BIBM.2015.7359773

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