title: Clinical knowledge-based inference model for early detection of acute lung injury.
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.
authors.affiliation: Department of Controls, Communication and Healthcare Informatics, Philips Research North America, Briarcliff Manor, NY 10510, USA. chbat@philips.com
authors.forename: Nicolas W
authors.initials: NW
authors.lastname: Chbat
authors.forename: Weiwei
authors.initials: W
authors.lastname: Chu
authors.forename: Monisha
authors.initials: M
authors.lastname: Ghosh
authors.forename: Guangxi
authors.initials: G
authors.lastname: Li
authors.forename: Man
authors.initials: M
authors.lastname: Li
authors.forename: Caitlyn M
authors.initials: CM
authors.lastname: Chiofolo
authors.forename: Srinivasan
authors.initials: S
authors.lastname: Vairavan
authors.forename: Vitaly
authors.initials: V
authors.lastname: Herasevich
authors.forename: Ognjen
authors.initials: O
authors.lastname: Gajic
mesh_terms: D055371:Acute Lung Injury / Q000175:diagnosis* / Q000473:pathology / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D011237:Predictive Value of Tests; D012984:Software