A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data.

ABSTRACT: Designing patient-specific follow-up strategies is key to personalized cancer care. Tools to assist doctors in treatment decisions and scheduling follow-ups based on patient preferences and medical data would be highly beneficial. These tools should incorporate realistic models of disease progression under treatment, multi-objective optimization of treatment strategies, and efficient algorithms to personalize follow-ups by considering patient history. We propose modeling cancer evolution using a Piecewise Deterministic Markov Process, where patients alternate between remission and relapse phases, and control the model via long-term cost function optimization. This considers treatment side effects, visit burden, and quality of life, using noisy blood marker measurements for feedback. Instea

SUBMITTER: de Saporta B 

PROVIDER: S-EPMC11658637 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature