Publication:
A Bayesian Approach to Explore Risk Factors for Respiratory Dysfunction in Intensive Care Unit Patient

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dc.citedby0
dc.contributor.authorShah N.B.N.H.en_US
dc.contributor.authorRazak N.N.B.A.en_US
dc.contributor.authorSamah A.B.A.en_US
dc.contributor.authorRazak N.A.B.A.en_US
dc.contributor.authorRamasamy A.en_US
dc.contributor.authorSuhaimi F.M.en_US
dc.contributor.authorChase J.G.en_US
dc.contributor.authorid58895331900en_US
dc.contributor.authorid37059587300en_US
dc.contributor.authorid58895113900en_US
dc.contributor.authorid56960052400en_US
dc.contributor.authorid16023154400en_US
dc.contributor.authorid36247893200en_US
dc.contributor.authorid35570524900en_US
dc.date.accessioned2024-10-14T03:19:17Z
dc.date.available2024-10-14T03:19:17Z
dc.date.issued2023
dc.description.abstractRespiratory dysfunction and failure are common in the intensive care unit (ICU)en_US
dc.description.abstractthey are often the primary reasons for ICU admission and affect length of stay, mortality, and cost. However, diagnosing respiratory dysfunction requires arterial blood gas values to calculate the partial pressure of arterial oxygen (PaO2) to a fraction of inspired oxygen (FiO2) or P/F ratio. These intermittent blood gas values may be difficult to obtain in some patients or where financial resources are limited. Its varying etiologies and lack of other specific biomarkers make diagnosing difficult without this measurement. Thus, in this study, we investigate commonly available parameters in the ICU for the classification of respiratory dysfunction without arterial blood gas values using a Bayesian network, an unsupervised structural learning method. Clinical data from selected patients in the Medical Information Mart for Intensive Care (MIMIC) III v1.4 database (N > 8900 patients) is used to create and validate these models. Bayesian network generated using the taboo order algorithm showed a satisfying performance in the classification of respiratory dysfunction. Results are compared to standard diagnosis with P/F ratio. The predictor variables selected could stratify respiratory dysfunction with 80% accuracy and 94% sensitivity. Hence, without using arterial blood gas values, these parameters could identify respiratory dysfunction in 90% of cases using Bayesian networks. � 2023, Politeknik Negeri Padang. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.30630/joiv.7.3-2.2370
dc.identifier.epage1056
dc.identifier.issue3-Feb
dc.identifier.scopus2-s2.0-85185471706
dc.identifier.spage1048
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85185471706&doi=10.30630%2fjoiv.7.3-2.2370&partnerID=40&md5=acaff976007103bd2bba144c5773eb1d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34363
dc.identifier.volume7
dc.pagecount8
dc.publisherPoliteknik Negeri Padangen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleInternational Journal on Informatics Visualization
dc.subjectBayesian network
dc.subjectclassification
dc.subjectintensive care unit
dc.subjectmachine learning
dc.subjectrespiratory failure
dc.titleA Bayesian Approach to Explore Risk Factors for Respiratory Dysfunction in Intensive Care Unit Patienten_US
dc.typeArticleen_US
dspace.entity.typePublication
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