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Clinical Knowledge-Based Inference Model for Early Detection of Acute Lung Injury

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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.

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Acknowledgments

This work was partially supported by the NIH Grant RC1 LM10468Z-01.

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Authors and Affiliations

  1. Department of Controls, Communication and Healthcare Informatics, Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY, 10510, USA

    Nicolas W. Chbat, Weiwei Chu, Monisha Ghosh, Caitlyn M. Chiofolo & Srinivasan Vairavan

  2. Mayo Clinic, Rochester, MN, USA

    Guangxi Li, Man Li, Vitaly Herasevich & Ognjen Gajic

Authors
  1. Nicolas W. Chbat
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  2. Weiwei Chu
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  3. Monisha Ghosh
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  4. Guangxi Li
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  5. Man Li
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  6. Caitlyn M. Chiofolo
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  7. Srinivasan Vairavan
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  8. Vitaly Herasevich
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  9. Ognjen Gajic
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Corresponding author

Correspondence to Nicolas W. Chbat.

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Associate Editor John H. Linehan oversaw the review of this article.

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Chbat, N.W., Chu, W., Ghosh, M. et al. Clinical Knowledge-Based Inference Model for Early Detection of Acute Lung Injury. Ann Biomed Eng 40, 1131–1141 (2012). https://doi.org/10.1007/s10439-011-0475-2

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  • DOI: https://doi.org/10.1007/s10439-011-0475-2

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