Journal article
Proximity of Cellular and Physiological Response Failures in Sepsis
IEEE journal of biomedical and health informatics, v 25(11), pp 4089-4097
Nov 2021
PMID: 34288881
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Sepsis is a devastating multi-stage health condition with a high mortality rate. Its complexity, prevalence, and dependency of its outcomes on early detection have attracted substantial attention from data science and machine learning communities. Previous studies rely on individual cellular and physiological responses representing organ system failures to predict health outcomes or the onset of different sepsis stages. However, it is known that organ systems' failures and dynamics are not independent events. In this study, we identify the dependency patterns of significant proximate sepsis-related failures of cellular and physiological responses using data from 12,223 adult patients hospitalized between July 2013 and December 2015. The results show that proximate failures of cellular and physiological responses create better feature sets for outcome prediction than individual responses. Our findings reveal the few significant proximate failures that play the major roles in predicting patients' outcomes. This study's results can be simply translated into clinical practices and inform the prediction and improvement of patients' conditions and outcomes.
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Details
- Title
- Proximity of Cellular and Physiological Response Failures in Sepsis
- Creators
- Ali Jazayeri - Drexel UniversityMuge Capan - Drexel UniversityJulie Ivy - North Carolina State UniversityRyan Arnold - Santa Ynez Valley Cottage HospitalChristopher C Yang - Drexel University
- Publication Details
- IEEE journal of biomedical and health informatics, v 25(11), pp 4089-4097
- Publisher
- IEEE
- Grant note
- NSF-1741306; IIS-1650531; DIBBs-1443019 / National Science Foundation (10.13039/100006435) 1833538 / National Science Foundation Smart and Connected Health 1R01LM012300-01A1; R01LM012300 / National Library of Medicine of the National Institutes of Health
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science; Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000714714200008
- Scopus ID
- 2-s2.0-85111039516
- Other Identifier
- 991019168096004721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Medical Informatics