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

Saved in:

Bibliographic Details
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
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Universiti Tenaga Nasional

id	my.uniten.dspace-23001
record_format	dspace
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

Prediction of sepsis progression in critical illness using artificial neural network

Similar Items