Abstract
Artificial ventilation is a crucial treatment to the patients in Intensive Care Unit. However, as the ventilator increasingly becomes more complex, it is not easy for less experienced clinicians to control the settings. The objective of the paper is to model the FiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> settings by clinician, using a neuro-fuzzy hybrid system. Two important issues, the interpretability and accuracy are balanced through an iterative reduction and tuning process. Fuzzy sets are merged according to their Hebbian importance, while membership functions are tuned through the Least-Mean-Square (LMS) algorithm. Effective, compact and interpretable fuzzy rules are generated and tested on real ventilation data, benchmarked with other neuro-fuzzy systems.
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