Clinical knowledge-based inference model for early detection of acute lung injury

Nicolas W. Chbat, Weiwei Chu, Monisha Ghosh, Guangxi Li, Man Li, Caitlyn M. Chiofolo, Srinivasan Vairavan, Vitaly Herasevich, Ognjen Gajic

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Acute lung injury (ALI) is a devastating complication of acute illness and one of the leading causes of multiple organ failure and mortality in the intensive care unit (ICU). The detection of this syndrome is limited due to the complexity of the disease, insufficient understanding of its development and progression, and the large amount of risk factors and modifiers. In this preliminary study, we present a novel mathematical model for ALI detection. It is constructed based on clinical and research knowledge using three complementary techniques: rule-based fuzzy inference systems, Bayesian networks, and finite state machines. The model is developed in Matlab®'s Simulink environment and takes as input pre-ICU and ICU data feeds of critically ill patients. Results of the simulation model were validated against actual patient data from an epidemiologic study. By appropriately combining all three techniques the performance attained is in the range of 71.7-92.6% sensitivity and 60.3-78.4% specificity.

Original languageEnglish (US)
Pages (from-to)1131-1141
Number of pages11
JournalAnnals of Biomedical Engineering
Volume40
Issue number5
DOIs
StatePublished - May 2012

Keywords

  • Acute lung injury (ALI)
  • Bayesian network (BN)
  • Clinical decision support (CDS)
  • Finite state machine (FSM)
  • Fuzzy inference system (FIS)
  • Intensive care unit (ICU)

ASJC Scopus subject areas

  • Biomedical Engineering

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