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Optimizing cancer patient follow-up strategies using simulations

Published on:

8 January 2024

Primary Category:

Optimization and Control

Paper Authors:

Benoîte de Saporta,

Aymar Thierry d'Argenlieu,

Régis Sabbadin,

Alice Cleynen

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Key Details

Models cancer progression between remission/relapse with few key parameters

Defines customizable quality of life cost function for patients

Adapts simulation algorithm to optimize model despite limited data

Simulation-based method outperforms exact but approximate discrete method

AI generated summary

Optimizing cancer patient follow-up strategies using simulations

This paper proposes using simulations to optimize cancer patient follow-up. A model of cancer progression is controlled by selecting treatments and visit times to minimize impacts on quality of life. Efficient simulation algorithms help optimize these complex decisions based on limited patient data over time.

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