Prediction of sepsis progression in critical illness using artificial neural network

Fatanah Mohamad Suhaimi, J. G. Chase, G. M. Shaw, U. K. Jamaludin, N. N. Razak

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationInternational Conference for Innovation in Biomedical Engineering and Life Sciences
EditorsFatimah Ibrahim, Mas Sahidayana Mohktar, Mohd Yazed Ahmad, Juliana Usman
PublisherSpringer Verlag
Pages127-132
Number of pages6
ISBN (Print)9789811002656
DOIs
Publication statusPublished - 01 Jan 2016
EventInternational Conference for Innovation in Biomedical Engineering and Life Sciences, ICIBEL 2015 - Putrajaya, Malaysia
Duration: 06 Dec 201508 Dec 2015

Publication series

NameIFMBE Proceedings
Volume56
ISSN (Print)1680-0737

Other

OtherInternational Conference for Innovation in Biomedical Engineering and Life Sciences, ICIBEL 2015
CountryMalaysia
CityPutrajaya
Period06/12/1508/12/15

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Biomedical Engineering

Cite this

Suhaimi, F. M., Chase, J. G., Shaw, G. M., Jamaludin, U. K., & Razak, N. N. (2016). Prediction of sepsis progression in critical illness using artificial neural network. In F. Ibrahim, M. S. Mohktar, M. Y. Ahmad, & J. Usman (Eds.), International Conference for Innovation in Biomedical Engineering and Life Sciences (pp. 127-132). (IFMBE Proceedings; Vol. 56). Springer Verlag. https://doi.org/10.1007/978-981-10-0266-3_26