Abstract
Mechanical ventilation can cause severe lung damage by inadequate adjustment of the ventilator. We introduce a Machine Learning approach to predict the pressure-dependent, non-linear lung compliance, a crucial parameter to estimate lung protective ventilation settings. Features were extracted by fitting a generally accepted lumped parameter model to time series data obtained from ARDS (adult respiratory distress syndrome) patients. Numerical prediction was performed by use of Gaussian processes, a probabilistic, non-parametric modeling approach for non-linear functions.
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References
Matamis, D., Lemaire, F., Harf, A., Brun-Buisson, C., Ansquer, J.C., Atlan, G.: Total respiratory pressure-volume curves in the adult respiratory distress syndrome. Chest 86, 58–66 (1984)
Jonson, B., Beydon, L., Brauer, K., Mansson, C., Valind, S., Grytzell, H.: Mechanics of respiratory system in healthy anesthetized humans with emphasis on viscoelastic properties. J. Appl. Physiol. 75, 132–140 (1993)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press, MA (2006)
Stahl, C.A., Möller, K., Schumann, S., Kuhlen, R., Sydow, M., Putensen, C., Guttmann, J.: Dynamic versus static respiratory mechanics in acute lung injury and acute respiratory distress syndrome. Crit. Care Med. 34, 2090–2098 (2006)
Ganzert, S., Guttmann, J., Kersting, K., Kuhlen, R., Putensen, C., Sydow, M., Kramer, S.: Analysis of respiratory pressure-volume curves in intensive care medicine using inductive machine learning. Artif. Intell. Med. 26, 69–86 (2002)
Quinlan, J.R.: Learning with continuous classes. In: Proceedings of the Australian Joint Conference on Artificial Intelligence, pp. 343–348. World Scientific, Singapore (1992)
Ganzert, S., Kramer, S., Möller, K., Steinmann, D., Guttmann, J.: Prediction of mechanical lung parameters using Gaussian process models. Technical report, TUM-I0911, Fakultät für Informatik, TU München (2009)
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Ganzert, S., Kramer, S., Möller, K., Steinmann, D., Guttmann, J. (2009). Prediction of Mechanical Lung Parameters Using Gaussian Process Models. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds) Artificial Intelligence in Medicine. AIME 2009. Lecture Notes in Computer Science(), vol 5651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02976-9_53
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DOI: https://doi.org/10.1007/978-3-642-02976-9_53
Publisher Name: Springer, Berlin, Heidelberg
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