HeartCast: Predicting acute hypotensive episodes in intensive care units
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Sun-Hee | - |
dc.contributor.author | Li, Lei | - |
dc.contributor.author | Faloutsos, Christos | - |
dc.contributor.author | Yang, Hyung-Jeong | - |
dc.contributor.author | Lee, Seong-Whan | - |
dc.date.accessioned | 2021-09-03T16:24:33Z | - |
dc.date.available | 2021-09-03T16:24:33Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2016-12 | - |
dc.identifier.issn | 1572-3127 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/86714 | - |
dc.description.abstract | Acute hypotensive episodes (AHEs) are serious clinical events in intensive care units (ICUs), and require immediate treatment to prevent patient injury. Reducing the risks associated with an AHE requires effective and efficient mining of data generated from multiple physiological time series. We propose HeartCast, a model that extracts essential features from such data to effectively predict AHE. HeartCast combines a non-linear support vector machine with best-feature extraction via analysis of the baseline threshold, quartile parameters, and window size of the physiological signals. Our approach has the following benefits: (a) it extracts the most relevant features; (b) it provides the best results for identification of an AHE event; (c) it is fast and scales with linear complexity over the length of the window; and (d) it can manage missing values and noise/outliers by using a best-feature extraction method. We performed experiments on data continuously captured from physiological time series of ICU patients (roughly 3 GB of processed data). HeartCast was found to outperform other state-of-the-art methods found in the literature with a 13.7% improvement in classification accuracy. (C) 2016 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.subject | ANESTHESIA | - |
dc.title | HeartCast: Predicting acute hypotensive episodes in intensive care units | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sun-Hee | - |
dc.contributor.affiliatedAuthor | Lee, Seong-Whan | - |
dc.identifier.doi | 10.1016/j.stamet.2016.07.001 | - |
dc.identifier.scopusid | 2-s2.0-84979743504 | - |
dc.identifier.wosid | 000390967700001 | - |
dc.identifier.bibliographicCitation | STATISTICAL METHODOLOGY, v.33, pp.1 - 13 | - |
dc.relation.isPartOf | STATISTICAL METHODOLOGY | - |
dc.citation.title | STATISTICAL METHODOLOGY | - |
dc.citation.volume | 33 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 13 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | ANESTHESIA | - |
dc.subject.keywordAuthor | Acute hypotensive episodes | - |
dc.subject.keywordAuthor | Feature selection | - |
dc.subject.keywordAuthor | Prediction | - |
dc.subject.keywordAuthor | Quartile parameters | - |
dc.subject.keywordAuthor | Physiological signal analysis | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.