artificial intelligence, automatic control, operational research … · 2018. 8. 16. · x....
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16/08/2018 1
Artificial Intelligence, Automatic Control, Operational Research for medicine, life
science and environment.
Alive Strategic Axis
Speaker: Louise Travé-Massuyès
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16/08/2018 216/08/2018 2LAAS-CNRS/ Laboratoire d’analyse et d’architecture des systèmes du CNRS 2
Decision and Optimisation in the living and environment fields
> Applied mathematics, computer science, artificial intelligence, automatic control, operations research have an everyday increasing role to play § More and more technology§ More and more data
Rationale: Develop constructive theoretical results and efficient computational algorithms for proposing control, supervision, diagnosis and optimization solutions
> Develop novel, personalized intervention methods for prevention, treatments, and drug administration
> Provide Machine Learning models to support predictions on the future development of diseases and also predicting the responses to specific therapies and preventive measures
> Identify novel patterns and health correlations in complex data sets> Design AI-assisted diagnosis to help physicians decide about the relevant care at
early stages of disease detection
Some examples:
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16/08/2018 316/08/2018 3LAAS-CNRS/ Laboratoire d’analyse et d’architecture des systèmes du CNRS 3
Automatic feedback-control of general anesthesia during surgery
Context : From anaesthesiologist-based open-loop control to automated closed-loop control (Multi-)objective:
- Medical point of view§ Fast induction§ Maintenance § Avoid drug overshoot§ Adapt to patient
characteristics
- Control theory point of view§ Positive system§ Slow/fast dynamics§ Patient uncertainty§ Actuator saturation§ Quantized sensor signal§ (aperiodic) sampling
Involved: I. QUEINNEC, S. TARBOURIECH, G. GARCIA (LAAS), M. MAZEROLLES (CHU Rangueil)
EEG
Bolus alloc function
(freq, conc, duration)
Positivity
Constraint continuous patient model
Pharmaco−kinetics
pro
po
fol
and
rem
ifen
tan
il
discrete/continuous
controllerStimulation
quantizerPupillarydilatation
flo
wra
tes
y1(t)
y2(k)
acquisitionconverterBIS
u0
u0
0−u
0−u u
sat(u)
0−u
u0
sat(u)−u
u
q(x)
x
Introduction Actuators and saturation Communication and sampling Conclusion
and a discrete-time dynamic output feedback controller
... interconnected with a discrete-time dynamic output feedback controller(
xc(tk+1) = Acxc(tk) + Bcy(tk) + Ec (u(tk))
u(tk) = Ccxc(tk) + Dcy(tk), 8k 2 N (10)
xc 2 Rnc , y 2 Rp and u 2 Rm are the state, the input and the output ofthe controller, respectively.
Ac , Bc , Cc , Dc , Ec are assumed to be constant and of appropriatedimensions.
Ec denotes an static anti-windup gain, and (u(tk)) is a vector-valueddecentralized dead-zone nonlinearity defined as follows:
(u(tk)) = sat(u(tk))� u(tk). (11)
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16/08/2018 416/08/2018 4LAAS-CNRS/ Laboratoire d’analyse et d’architecture des systèmes du CNRS 4
Development of neural networks for 2D - quality control during radiotherapy treatment (Project DIVIN)
Context : External radiotherapy treatment.
(Electronic portal imaging
devEPIDice) è Now, not used for improving the control before (quality
control of the machine) or during the
treatment
è If used, possibility of determination- Absorbed dose truly received by the
patient during all treatment sessions
- Re-computing the treatment
plans in case of discrepancies
Objective : 2D(3D) absorbed dose reconstruction
è spatial configuration to reproduce spatial diffusion
inputs
outputs
Learning phase =
treatment planning
system (TPS)
absorbed dose
distribution considered
like the truth
EPID imageReconstructed
absorbed dose
(ANN)
Planned
absorbed dose
(TPS): target
Example : Intensity-modulated radiotherapy (10 iI/O datasets)
Detection of leaf mis-position
Comparison of gindex
Involved : F. CHATRIE (PhD, LAAS), MV LE LANN( LAAS/DISCO)
X. FRANCERIES (ONCOPOLE)
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16/08/2018 516/08/2018 5LAAS-CNRS/ Laboratoire d’analyse et d’architecture des systèmes du CNRS 5J-C. Billaut – EURO, Vilnius, July 2012
Combinatorial Optimization for the analysis of big amounts of biomedical data
Involved : J. MONCEL (LAAS), N. BRAUNER, J. DARLEY (G-SCOP)
> Big data to be analyzed for biomedical diagnosis> Goal:
§ Characterize two classes: patients with pathology and patients without § Provide easily interpretable rules of the form IF condition THEN class 0/1§ Handle discrete and continuous variables
> Minimize the number of cutting points to binarize continuous variables> Find the support of observations (patterns covering all the set of clinical cases):
set cover problem> Reduce the support if too big: graph partitioning based on graph density to
identify groups of patterns and select one single pattern to represent all the group
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Artificial Intelligence, Automatic Control, Operational Research for medicine, life
science and environment.
Alive Strategic Axis
Speaker: Louise Travé-Massuyès