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Prediction of sepsis progression in critical illness using artificial neural network

Biomarkers; 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 treatment

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Main Authors: Suhaimi F.M., Chase J.G., Shaw G.M., Jamaludin U.K., Razak N.N.
Other Authors: 36247893200
Format: Conference Paper
Published: Springer Verlag 2023
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id my.uniten.dspace-23001
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spelling my.uniten.dspace-230012023-05-29T14:14:03Z Prediction of sepsis progression in critical illness using artificial neural network Suhaimi F.M. Chase J.G. Shaw G.M. Jamaludin U.K. Razak N.N. 36247893200 35570524900 7401773560 55330889600 37059587300 Biomarkers; 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 treatment Early 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. Final 2023-05-29T06:14:03Z 2023-05-29T06:14:03Z 2016 Conference Paper 10.1007/978-981-10-0266-3_26 2-s2.0-84952790621 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952790621&doi=10.1007%2f978-981-10-0266-3_26&partnerID=40&md5=ee66a2c6cb29755ee7f267e276f04112 https://irepository.uniten.edu.my/handle/123456789/23001 56 127 132 Springer Verlag Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Biomarkers; 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 treatment
author2 36247893200
author_facet 36247893200
Suhaimi F.M.
Chase J.G.
Shaw G.M.
Jamaludin U.K.
Razak N.N.
format Conference Paper
author Suhaimi F.M.
Chase J.G.
Shaw G.M.
Jamaludin U.K.
Razak N.N.
spellingShingle Suhaimi F.M.
Chase J.G.
Shaw G.M.
Jamaludin U.K.
Razak N.N.
Prediction of sepsis progression in critical illness using artificial neural network
author_sort Suhaimi F.M.
title Prediction of sepsis progression in critical illness using artificial neural network
title_short Prediction of sepsis progression in critical illness using artificial neural network
title_full Prediction of sepsis progression in critical illness using artificial neural network
title_fullStr Prediction of sepsis progression in critical illness using artificial neural network
title_full_unstemmed Prediction of sepsis progression in critical illness using artificial neural network
title_sort prediction of sepsis progression in critical illness using artificial neural network
publisher Springer Verlag
publishDate 2023
_version_ 1806427675912306688
score 13.214268

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