Expert knowledge-based strategies for ventilator parameter setting and stepless adaptive adjustment_Journal of Biomedical Engineering

Authors：

WANG Yongyan ^1,2,3 ,  MA Songhua ^1,2,3 , HU Tianliang ^1,2,3 ,  MA Dedong ^4,5 , LIAN Xianhui ^1,2,3 , WANG Shuai ^1,2,3 , ZHANG Jiguo ^1,2,3

1. School of Mechanical Engineering, Shandong University, Jinan 250061, P. R. China;
2. Key Laboratory of High Efficiency and Clean Mechanical Manufacture at Shandong University, Ministry of Education, Jinan 250061, P. R. China;
3. National Demonstration Center for Experimental Mechanical Engineering Education, Jinan 250061, P. R. China;
4. Qilu Hospital of Shandong University, Jinan 250012, P. R. China;
5. Key Laboratory of Otolaryngology, Shandong University, Jinan 250012, P. R. China;

Corresponding author：

MA Songhua, Email: msh_1216@sdu.edu.cn; MA Dedong, Email: ma@qiluhuxi.com

Keywords：

Ventilator; Parameter setting; Adaptive ventilation; Stepless adjustment

DOI：

10.7507/1001-5515.202209015

Video：

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The setting and adjustment of ventilator parameters need to rely on a large amount of clinical data and rich experience. This paper explored the problem of difficult decision-making of ventilator parameters due to the time-varying and sudden changes of clinical patient’s state, and proposed an expert knowledge-based strategies for ventilator parameter setting and stepless adaptive adjustment based on fuzzy control rule and neural network. Based on the method and the real-time physiological state of clinical patients, we generated a mechanical ventilation decision-making solution set with continuity and smoothness, and automatically provided explicit parameter adjustment suggestions to medical personnel. This method can solve the problems of low control precision and poor dynamic quality of the ventilator’s stepwise adjustment, handle multi-input control decision problems more rationally, and improve ventilation comfort for patients.

Citation： WANG Yongyan, MA Songhua, HU Tianliang, MA Dedong, LIAN Xianhui, WANG Shuai, ZHANG Jiguo. Expert knowledge-based strategies for ventilator parameter setting and stepless adaptive adjustment. Journal of Biomedical Engineering, 2023, 40(5): 945-952. doi: 10.7507/1001-5515.202209015 Copy

1.	Epstein S K. Weaning from ventilatory support. Curr Opin Crit Care, 2009, 15(1): 36-43.
2.	Dojat M, Brochard L, Lemaire F, et al. A knowledge-based system for assisted ventilation of patients in intensive care units. J Clin Monit Comput, 1992, 9(4): 239-250.
3.	Tehrani F T, Roum J H. Flex: A new computerized system for mechanical ventilation. J Clin Monit Comput, 2008, 22(2): 121-130.
4.	Tzavaras A, Weller P, Spyropoulos B. A neuro-fuzzy controller for the estimation of tidal volume and respiration frequency ventilator settings for COPD patients ventilated in control mode// 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Lyon: IEEE, 2007: 3765-3768.
5.	Morris A H. Developing and implementing computerized protocols for standardization of clinical decisions. Ann Intern Med, 2000, 132(5): 373-383.
6.	Rutledge G W, Thomsen G E, Farr B R, et al. The design and implementation of a ventilator-management advisor. Artif Intell Med, 1993, 5(1): 67-82.
7.	Rees S E, Allerod C, Murley D, et al. Using physiological models and decision theory for selecting appropriate ventilator settings. J Clin Monit Comput, 2006, 20(6): 421-429.
8.	East T D, Heermann L K, Bradshaw R L, et al. Efficacy of computerized decision support for mechanical ventilation: results of a prospective multi-center randomized trial. Proc AMIA Symp, 1999: 251-255.
9.	Lellouche F, Mancebo J, Jolliet P, et al. A multicenter randomized trial of computer-driven protocolized weaning from mechanical ventilation. Am J Resp Crit Care, 2006, 174(8): 894-900.
10.	Mahmud F B, Yusof M M, Noah S A. Ontological based clinical decision support system (CDSS) for weaning ventilator in intensive care unit (ICU)// International Conference on Electrical Engineering and Informatics (ICEEI). Bandung: IEEE, 2011: 1-5.
11.	Hatko R, Schädler D, Mersmann S, et al. Implementing an automated ventilation guideline using the semantic wiki KnowWE// ten Teije A. Knowledge engineering and knowledge management. EKAW 2012. Lecture notes in computer science. Berlin, Heidelberg: Springer, 2012, 7603: 363-372.
12.	Hernández-Torruco J, Canul-Reich J, Román D L. Rule based classifiers for diagnosis of mechanical ventilation in Guillain-Barré Syndrome// Hernández-Torruco J, Canul-Reich J, Román D L. Advances in intelligent systems and computing. Cham: Springer, 2017, 620: 181-188.
13.	Battaglini D, Sottano M, Ball L, et al. Ten golden rules for individualized mechanical ventilation in acute respiratory distress syndrome. J Intens Med, 2021, 1(1): 42-51.
14.	Sittig D F, Pace N L, Gardner R M, et al. Implementation of a computerized patient advice system using the HELP clinical information system. Comput Biomed Res, 1989, 22(5): 474-487.
15.	McKinley B A, Moore F A, Sailors R M, et al. Computerized decision support for mechanical ventilation of trauma induced ARDS: results of a randomized clinical trial. J Trauma Acute Care, 2001, 50(3): 415-425.
16.	Strickland J H, Hasson J H. A computer-controlled ventilator weaning system. Chest, 1991, 100(4): 1096-1099.
17.	王春飞, 陈战. 一种基于模型的机械通气模糊逻辑决策系统. 生物医学工程学杂志, 2016, 33(4): 786-793.
18.	Rees S E, Kjærgaard S, Thorgaard P, et al. The automatic lung parameter estimator (ALPE) system: non-invasive estimation of pulmonary gas exchange parameters in 10-15 minutes. J Clin Monit Comput, 2002, 17(1): 43-52.
19.	Smith B W, Rees S E, Karbing D S, et al. Quantitative assessment of pulmonary shunt and ventilation-perfusion mismatch without a blood sample// International Conference of the IEEE Engineering in Medicine & Biology Society. Lyon: IEEE, 2007: 4255-4258.
20.	Thomsen L P, Karbing D S, Smith B W, et al. Clinical refinement of the automatic lung parameter estimator (ALPE). J Clin Monit Comput, 2013, 27(3): 341-350.
21.	Allerød C, Karbing D S, Thorgaard P, et al. Variability of preference toward mechanical ventilator settings: A model-based behavioral analysis. J Crit Care, 2011, 26(6): 637.e5-637.e12.
22.	Karbing D S, Allerød C, Thomsen L P, et al. Retrospective evaluation of a decision support system for controlled mechanical ventilation. Med Biol Eng Comput, 2012, 50(1): 43-51.
23.	Allerød C, Rees S E, Rasmussen B S, et al. A decision support system for suggesting ventilator settings: retrospective evaluation in cardiac surgery patients ventilated in the ICU. Comput Meth Prog Bio, 2008, 92(2): 205-212.
24.	Network A R D S. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. New Engl J Med, 2000, 342(18): 1301-1308.
25.	Marini J J, Gattinoni L. Ventilatory management of acute respiratory distress syndrome: a consensus of two. Crit Care Med, 2004, 32(1): 250-255.
26.	Deja M, Hommel M, Weber-Carstens S, et al. Evidence-based therapy of severe acute respiratory distress syndrome: an algorithm-guided approach. J Int Med Res, 2008, 36(2): 211-221.
27.	Mao C, Wong D T, Slutsky A S, et al. A quantitative assessment of how Canadian intensivists believe they utilize oxygen in the intensive care unit. Crit Care Med, 1999, 27(12): 2806-2811.
28.	Young M P, Manning H L, Wilson D L, et al. Ventilation of patients with acute lung injury and acute respiratory distress syndrome: has new evidence changed clinical practice?. Crit Care Med, 2004, 32(6): 1260-1265.
29.	Esteban A, Ferguson N D, Meade M O, et al. Evolution of mechanical ventilation in response to clinical research. Am J Resp Critical Care, 2008, 177(2): 170-177.

1. Epstein S K. Weaning from ventilatory support. Curr Opin Crit Care, 2009, 15(1): 36-43.
2. Dojat M, Brochard L, Lemaire F, et al. A knowledge-based system for assisted ventilation of patients in intensive care units. J Clin Monit Comput, 1992, 9(4): 239-250.
3. Tehrani F T, Roum J H. Flex: A new computerized system for mechanical ventilation. J Clin Monit Comput, 2008, 22(2): 121-130.
4. Tzavaras A, Weller P, Spyropoulos B. A neuro-fuzzy controller for the estimation of tidal volume and respiration frequency ventilator settings for COPD patients ventilated in control mode// 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Lyon: IEEE, 2007: 3765-3768.
5. Morris A H. Developing and implementing computerized protocols for standardization of clinical decisions. Ann Intern Med, 2000, 132(5): 373-383.
6. Rutledge G W, Thomsen G E, Farr B R, et al. The design and implementation of a ventilator-management advisor. Artif Intell Med, 1993, 5(1): 67-82.
7. Rees S E, Allerod C, Murley D, et al. Using physiological models and decision theory for selecting appropriate ventilator settings. J Clin Monit Comput, 2006, 20(6): 421-429.
8. East T D, Heermann L K, Bradshaw R L, et al. Efficacy of computerized decision support for mechanical ventilation: results of a prospective multi-center randomized trial. Proc AMIA Symp, 1999: 251-255.
9. Lellouche F, Mancebo J, Jolliet P, et al. A multicenter randomized trial of computer-driven protocolized weaning from mechanical ventilation. Am J Resp Crit Care, 2006, 174(8): 894-900.
10. Mahmud F B, Yusof M M, Noah S A. Ontological based clinical decision support system (CDSS) for weaning ventilator in intensive care unit (ICU)// International Conference on Electrical Engineering and Informatics (ICEEI). Bandung: IEEE, 2011: 1-5.
11. Hatko R, Schädler D, Mersmann S, et al. Implementing an automated ventilation guideline using the semantic wiki KnowWE// ten Teije A. Knowledge engineering and knowledge management. EKAW 2012. Lecture notes in computer science. Berlin, Heidelberg: Springer, 2012, 7603: 363-372.
12. Hernández-Torruco J, Canul-Reich J, Román D L. Rule based classifiers for diagnosis of mechanical ventilation in Guillain-Barré Syndrome// Hernández-Torruco J, Canul-Reich J, Román D L. Advances in intelligent systems and computing. Cham: Springer, 2017, 620: 181-188.
13. Battaglini D, Sottano M, Ball L, et al. Ten golden rules for individualized mechanical ventilation in acute respiratory distress syndrome. J Intens Med, 2021, 1(1): 42-51.
14. Sittig D F, Pace N L, Gardner R M, et al. Implementation of a computerized patient advice system using the HELP clinical information system. Comput Biomed Res, 1989, 22(5): 474-487.
15. McKinley B A, Moore F A, Sailors R M, et al. Computerized decision support for mechanical ventilation of trauma induced ARDS: results of a randomized clinical trial. J Trauma Acute Care, 2001, 50(3): 415-425.
16. Strickland J H, Hasson J H. A computer-controlled ventilator weaning system. Chest, 1991, 100(4): 1096-1099.
17. 王春飞, 陈战. 一种基于模型的机械通气模糊逻辑决策系统. 生物医学工程学杂志, 2016, 33(4): 786-793.
18. Rees S E, Kjærgaard S, Thorgaard P, et al. The automatic lung parameter estimator (ALPE) system: non-invasive estimation of pulmonary gas exchange parameters in 10-15 minutes. J Clin Monit Comput, 2002, 17(1): 43-52.
19. Smith B W, Rees S E, Karbing D S, et al. Quantitative assessment of pulmonary shunt and ventilation-perfusion mismatch without a blood sample// International Conference of the IEEE Engineering in Medicine & Biology Society. Lyon: IEEE, 2007: 4255-4258.
20. Thomsen L P, Karbing D S, Smith B W, et al. Clinical refinement of the automatic lung parameter estimator (ALPE). J Clin Monit Comput, 2013, 27(3): 341-350.
21. Allerød C, Karbing D S, Thorgaard P, et al. Variability of preference toward mechanical ventilator settings: A model-based behavioral analysis. J Crit Care, 2011, 26(6): 637.e5-637.e12.
22. Karbing D S, Allerød C, Thomsen L P, et al. Retrospective evaluation of a decision support system for controlled mechanical ventilation. Med Biol Eng Comput, 2012, 50(1): 43-51.
23. Allerød C, Rees S E, Rasmussen B S, et al. A decision support system for suggesting ventilator settings: retrospective evaluation in cardiac surgery patients ventilated in the ICU. Comput Meth Prog Bio, 2008, 92(2): 205-212.
24. Network A R D S. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. New Engl J Med, 2000, 342(18): 1301-1308.
25. Marini J J, Gattinoni L. Ventilatory management of acute respiratory distress syndrome: a consensus of two. Crit Care Med, 2004, 32(1): 250-255.
26. Deja M, Hommel M, Weber-Carstens S, et al. Evidence-based therapy of severe acute respiratory distress syndrome: an algorithm-guided approach. J Int Med Res, 2008, 36(2): 211-221.
27. Mao C, Wong D T, Slutsky A S, et al. A quantitative assessment of how Canadian intensivists believe they utilize oxygen in the intensive care unit. Crit Care Med, 1999, 27(12): 2806-2811.
28. Young M P, Manning H L, Wilson D L, et al. Ventilation of patients with acute lung injury and acute respiratory distress syndrome: has new evidence changed clinical practice?. Crit Care Med, 2004, 32(6): 1260-1265.
29. Esteban A, Ferguson N D, Meade M O, et al. Evolution of mechanical ventilation in response to clinical research. Am J Resp Critical Care, 2008, 177(2): 170-177.

Journal of Biomedical Engineering

Expert knowledge-based strategies for ventilator parameter setting and stepless adaptive adjustment

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