Approximate dynamic programming algorithms for optimal dosage decisions in controlled ovarian hyperstimulation

In the controlled ovarian hyperstimulation (COH) treatment, clinicians monitor the patients’ physiological responses to gonadotropin administration to tradeoff between pregnancy probability and ovarian hyperstimulation syndrome (OHSS). We formulate the dosage control problem in the COH treatment as a stochastic dynamic program and design approximate dynamic programming (ADP) algorithms to overcome the well-known curses of dimensionality in Markov decision processes (MDP). Our numerical experiments indicate that the piecewise linear (PWL) approximation ADP algorithms can obtain policies that are very close to the one obtained by the MDP benchmark with significantly less solution time.

► Model the dosage decisions in COH treatment as a stochastic optimization problem.
► Design efficient approximate dynamic programming (ADP) solution algorithms.
► Compare ADP policies with the Markov decision processes (MDP) benchmark.
► Apply cutting-edge OR in evidence-based and data-driven clinical decision making.