A model-based decision support system for mechanical ventilation using fuzzy logic

被引：0

作者：

Wang C. ^{[1
]}

Zhang G. ^{[1
]}

Wu T. ^{[2
]}

机构：

[1] The Chenggong Affiliated Hospital of Xiamen University, Xiamen

[2] Institute of Medical Equipment, Academy of Military Medical Science, Tianjin

来源：

International Journal of Simulation: Systems, Science and Technology | 2016年 / 17卷 / 36期

关键词：

Fuzzy logic; Mechanical ventilation; Pulmonary gas exchange model;

D O I：

10.5013/IJSSST.a.17.36.27

中图分类号：

学科分类号：

摘要：

In clinical practice, the mechanical ventilation is a very important aided method to improve patients’ breath, so parameter setting of ventilator directly affects pulmonary gas exchange. In this report, we try to build a decision support system based on the gas exchange model for mechanical ventilation using fuzzy logic. The gas exchange mathematic model can simulate individual patient’s pulmonary gas exchange, and can help doctors to learn patient’s exactly situation. The system uses fuzzy logic algorithm, utilizing the measurement of patient, and generates the ventilator settings respond to individual patient. The system was retrospectively evaluated in 10 intensive care patient cases, with mathematic models fitted to the retrospective data, and then used to simulate patient response to changes in therapy. Compared to the ventilator settings set as part of routine clinical care, the system lowers inspired oxygen fraction and breath work and improves gas exchange with the model simulated outcome. © 2016, UK Simulation Society. All rights reserved.

引用

页码：27.1 / 27.7

共 14 条

[1]

Iotti G.V., Closed-loop control mechanical ventilation, Respir Care Clin N Am, 7, pp. 341-515, (2001)

[2]

Bernstein D.B., Nguyen B., Allen G.B., Bates J.H., Elucidating the fuzziness in physician decision making in ARDS, J Clin Monit Comput., 27, pp. 357-363, (2013)

[3]

McKinley B.A., Moore F.A., Sailors R.M., Computerized decision support for mechanical ventilation of trauma induced ARDS: Results of a randomized clinical trial, J Trauma, 50, pp. 415-425, (2001)

[4]

Kwok H.F., Linkens D.A., Mahfouf M., Mills G.H., Rule-base derivation for intensive care ventilator control using ANFIS, Artif Intell Med, 29, pp. 185-201, (2003)

[5]

Bouadma L., Lellouche F., Cabello B., Taille S., Mancebo J., Dojat M., Brochard L., Computer-driven management of prolonged mechanical ventilation and weaning: A pilot study, Intensive Care Med, 31, pp. 1446-1450, (2005)

[6]

Dojat M., Harf A., Touchard D., Lemaire F., Brochard L., Clinical evaluation of a computer-controlled pressure support mode, Am J Respir Crit Care Med, 161, pp. 1161-1166, (2000)

[7]

Karbing D.S., Allerod C., Thomsen L.P., Retrospective evaluation of a decision support system for controlled mechanical ventilation, Med Biol Eng Comput, 50, pp. 43-51, (2012)

[8]

Rees S.E., Allerod C., Murley D., Zhao Y., Using physiological models and decision theory for selecting appropriate ventilators setting, Journal of Clinical Monitoring and Computing, 20, pp. 421-429, (2006)

[9]

Rees S.E., Kjaergaard S., Perthorgaard P., The automatic lung parameter estimator (ALPE) system: Non-invasive estimation of pulmonary gas exchange parameters in 10-15 minutes, Journal of Clinical Monitoring and Computing, 17, pp. 43-52, (2002)

[10]

Andreassen S., Rees S.E., Mathematical models of oxygen and carbon dioxide storage and transport: Interstitial fluid and tissue stores and whole-body transport, Crit Rev Biomed Eng, 33, 3, pp. 265-298, (2005)

← 1 2 →