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Automated mechanical ventilation: adapting decision making to different disease states

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Abstract

The purpose of the present study is to introduce a novel methodology for adapting and upgrading decision-making strategies concerning mechanical ventilation with respect to different disease states into our fuzzy-based expert system, AUTOPILOT-BT. The special features are: (1) Extraction of clinical knowledge in analogy to the daily routine. (2) An automated process to obtain the required information and to create fuzzy sets. (3) The controller employs the derived fuzzy rules to achieve the desired ventilation status. For demonstration this study focuses exclusively on the control of arterial CO2 partial pressure (paCO2). Clinical knowledge from 61 anesthesiologists was acquired using a questionnaire from which different disease-specific fuzzy sets were generated to control paCO2. For both, patients with healthy lung and with acute respiratory distress syndrome (ARDS) the fuzzy sets show different shapes. The fuzzy set “normal”, i.e., “target paCO2 area”, ranges from 35 to 39 mmHg for healthy lungs and from 39 to 43 mmHg for ARDS lungs. With the new fuzzy sets our AUTOPILOT-BT reaches the target paCO2 within maximal three consecutive changes of ventilator settings. Thus, clinical knowledge can be extended, updated, and the resulting mechanical ventilation therapies can be individually adapted, analyzed, and evaluated.

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Acknowledgments

This work was supported by Bundesministerium für Bildung und Forschung (Grant 1781X08 MOTiF-A), and Dräger Medical, Lübeck.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Furtwangen University, Villingen-Schwenningen Campus, Jakob Kienzle Straße 17, Villingen-Schwenningen, 78054, Germany

    S. Lozano-Zahonero & K. Möller

  2. Department of Anesthesiology, Section for Experimental Anesthesiology, University Medical Center, Freiburg, Germany

    D. Gottlieb & J. Guttmann

  3. Department of Anesthesiology, Section for Surgical Intensive Care, Kantonsspital Luzern, Lucerne, Switzerland

    C. Haberthür

Authors
  1. S. Lozano-Zahonero
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  2. D. Gottlieb
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  3. C. Haberthür
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  4. J. Guttmann
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  5. K. Möller
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Correspondence to S. Lozano-Zahonero.

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Lozano-Zahonero, S., Gottlieb, D., Haberthür, C. et al. Automated mechanical ventilation: adapting decision making to different disease states. Med Biol Eng Comput 49, 349–358 (2011). https://doi.org/10.1007/s11517-010-0712-0

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  • DOI: https://doi.org/10.1007/s11517-010-0712-0

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