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Stochastic control for medical treatment optimization

De Benoîte de Saporta

Apparaît dans la collection : A Random Walk in the Land of Stochastic Analysis and Numerical Probability / Une marche aléatoire dans l'analyse stochastique et les probabilités numériques

We are interested in monitoring patients in remission from cancer. Our aim is to detect their relapses as soon as possible, as well as detect the type of relapse, to decide on the appropriate treatment to be given. Available data are some marker level of the rate of cancerous cells in the blood which evolves continuously but is measured at discrete (large) intervals and through noise. The patient's state of health is modeled by a piecewise deterministic Markov process (PDMP). Several decisions must be taken from these incomplete observations: what treatment to give, and when to schedule the next medical visit. After presenting a suitable class of controlled PDMPs to model this situation, I will describe the corresponding stochastic control problem and will present the resolution strategy that we adopted. The objective is to obtain an approximation of the value function (optimal performance) as well as build an explicit policy applicable in practice and as close to optimality as possible. The results will be illustrated by simulations calibrated on a cohort of a clinical trial on multiple myeloma provided by the Center of Cancer Research in Toulouse.

Informations sur la vidéo

Date de captation 06/09/2023
Date de publication 27/09/2023
Institut CIRM
Licence CC BY NC ND
Langue Anglais
Audience Chercheurs, Doctorants
Réalisateur(s) Luca Recanzone
Format MP4

Données de citation

DOI 10.24350/CIRM.V.20088403
Citer cette vidéo de Saporta, Benoîte (06/09/2023). Stochastic control for medical treatment optimization. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.20088403
URL https://dx.doi.org/10.24350/CIRM.V.20088403

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Codes MSC

Document(s)

https://www.cirm-math.fr/RepOrga/2390/Slides/Benoite_de_Saporta.pdf

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