O.
Caelen*, G. Bontempi*, F. Clément§, E. Coussaert#, L. Barvais#
*Machine
Learning Group, Computer Science, Université Libre de Bruxelles, Belgium; §Mexys-SA,
Mons, Belgium #Erasmus Hospital
Anaesthesia Departments, Université Libre de Bruxelles, Belgium;
Background and Goal of the Study:
BIS
guided TCI anaesthesia archives were used to design a BIS-Propofol closed-loop
controller by machine learning algorithms. The approach allows adaptive control
capabilities and combines the information of several input variables measuring
the patient condition and the operation state. The purpose of the study was to
compare, in a simulation setting, the outputs of our controller with an
expert-based closed-loop controller currently used in real conditions with
acceptable performances. This expert-based closed-loop controller titrates
Propofol and Remifentanil automatically according to a predefined range of BIS
and hemodynamic values.
Materials and Methods:
Data
collected on 965 chirurgical interventions were used to search the best values
of the closed-loop parameters. To propose the best target of Propofol for
controlling the BIS, our closed-loop uses the following variables: the current
BIS value, the current target of Propofol, the current target of Remifentanil
and the age and the weight of the patient. A Lazy Learning algorithm [1] was
used as machine learning method. The control action proposed by our closed-loop
controller was compared to the behavior of an expert-based closed-loop
controller in 18 real archived TCI anaesthesias. Let PE (predictive error) be
equal to the target of Propofol (μg/ml) proposed by the existing
closed-loop minus the target of Propofol (μg/ml) proposed by our new
closed-loop. To assess the actions of our new closed-loop, the following
measures are computed: MDPE (median of the predictive error), MDAPE (median of
the absolute predictive error) and the NMSE (normalized mean squared error).
Results:
|
MDPE |
MDAPE |
NMSE |
|
-6.1% |
10.1% |
0.19 |
Discussions:
The Propofol titration behavior of the two
controllers is significantly different (P<<0.01) but the small value of
the MDAPE means that this difference is reasonable. The average of the BIS
values, when controlled by the existing closed-loop, is known to be sometimes
too low and the negative sign of the MDPE is thus a promising result. The NMSE
is a well known positive statistical measure of the prediction error where the
value of one indicates the performance of the simplest prediction model and the
small value of the NMSE is thus also a hopeful result.
Conclusion(s):
The
simulation tests appear to be promising and the next step of this study will be
a test in real conditions.
Reference: