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Prediction of sepsis progression in critical illness using artificial neural network

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dc.citedby1
dc.contributor.authorSuhaimi F.M.en_US
dc.contributor.authorChase J.G.en_US
dc.contributor.authorShaw G.M.en_US
dc.contributor.authorJamaludin U.K.en_US
dc.contributor.authorRazak N.N.en_US
dc.contributor.authorid36247893200en_US
dc.contributor.authorid35570524900en_US
dc.contributor.authorid7401773560en_US
dc.contributor.authorid55330889600en_US
dc.contributor.authorid37059587300en_US
dc.date.accessioned2023-05-29T06:14:03Z
dc.date.available2023-05-29T06:14:03Z
dc.date.issued2016
dc.descriptionBiomarkers; Biomedical engineering; Decision making; Intensive care units; Neural networks; Antimicrobial therapy; Clinical guideline; Patient condition; Provide guidances; Sensitivity and specificity; Sepsis; Sepsis score; Septic shocks; Patient treatmenten_US
dc.description.abstractEarly treatment of sepsis can reduce mortality and improve a patient condition. However, the lack of clear information and accurate methods of diagnosing sepsis at an early stage makes it become a significant challenge. The decision to start, continue or stop antimicrobial therapy is normally base on clinical judgment since blood cultures will be negative in the majority of cases of septic shock or sepsis. However, clinical guidelines are still required to provide guidance for the clinician caring for a patient with severe sepsis or septic shock. Guidelines based on patient�s unique set of clinical variables will help a clinician in the process of decision making of suitable treatment for the particular patient. Therefore, biomarkers for sepsis diagnosis with a reasonable sensitivity and specificity are a requirement in ICU settings, as a guideline for the treatment. Moreover, the biomarker should also allow availability in real-time and prediction of sepsis progression to avoid delay in treatment and worsen the patient condition. � International Federation for Medical and Biological Engineering 2016.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-981-10-0266-3_26
dc.identifier.epage132
dc.identifier.scopus2-s2.0-84952790621
dc.identifier.spage127
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84952790621&doi=10.1007%2f978-981-10-0266-3_26&partnerID=40&md5=ee66a2c6cb29755ee7f267e276f04112
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23001
dc.identifier.volume56
dc.publisherSpringer Verlagen_US
dc.sourceScopus
dc.sourcetitleIFMBE Proceedings
dc.titlePrediction of sepsis progression in critical illness using artificial neural networken_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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