Predictive data mining techniques in anaesthesia
d'anesthésie de l'hôpital ERASME, ARTEMI and AIRLAB,
Politecnico di Milano (Italy)
Funding: FIRST Europe
The goal of the project is to develop data mining techniques
to analyse the massive datasets collected by
the "TOOLBOX" software, currently used by the anestehesists of the
during the surgical interventions.
Data concerns the monitored state of the patient (e.g. blood pressure,
the rate of heartbeat),
the type and concentrations of drugs, the actions of the anesthesist.
The resulting predictive models are expected to
support anesthesists with high-level information about the
course of the operation.
Caelen (Machine Learning Group - Computer Science Department -
Université Libre de Bruxelles