Authors:
Hugo Boisaubert
1
;
Lucas Vincent
1
;
Corinne Lejus-Bourdeau
2
;
3
and
Christine Sinoquet
1
Affiliations:
1
Research Laboratory of Digital Science in Nantes (LS2N) / UMR CNRS 6004, University of Nantes,
;
2
Experimental Universitary Laboratory for Simulation in Intensive Care (LESiMU) in Nantes,
;
3
rue de la Houssinière, Nantes, France
Keyword(s):
Computer-assisted Medical Training, Virtual Patient, Operating Room, Anaesthesia, Reactive Scenario, Simulation, Prediction, Case-based Reasoning, Data Mining, Pattern Recognition, E-health Record, Event Trace, Multivariate Time Series.
Abstract:
Half a million surgeries are performed every day around the world, which places safety and quality at the heart of global health issues. In this context, we introduce a novel approach, SVP-OR (Simulation of Virtual Patient at the Operating Room), designed for digital training support. For this purpose, we must evolve the physiological parameters of a virtual patient submitted to the actions of a user (trainee), and of a virtual medical team. We formulate the problem as a case-based reasoning approach in which (i) we identify real patients whose anaesthetic profiles show a region similar to the recent history of the virtual patient and (ii) we predict the near future of the virtual patient (a multivariate time series) from the multivariate time series of the most similar real patients. The first contribution in this paper consists in the design of a contextualized multidimensional pattern recognition approach. Our second contribution is the development of a generic framework based on
the concept of contextualized multidimensional pattern, to predict the evolution of the virtual patient. In a third contribution, we instantiate our framework, and we evaluate and compare the realism of two predictive strategies.
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