# Patient level simulation and reinforcement learning to discover novel strategies for treating ovarian cancer

arXiv: https://arxiv.org/abs/2110.11872 · HTML: https://mlsanity.com/paper/2110.11872/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 38 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Simulation-based reinforcement learning for real-world autonomous driving](https://mlsanity.com/paper/1911.12905/) (2019)
- [A Markov decision process approach to optimizing cancer therapy using multiple modalities](https://mlsanity.com/paper/1706.09481/) (2017)

## Abstract (arXiv)

The prognosis for patients with epithelial ovarian cancer remains dismal despite improvements in survival for other cancers. Treatment involves multiple lines of chemotherapy and becomes increasingly heterogeneous after first-line therapy. Reinforcement learning with real-world outcomes data has the potential to identify novel treatment strategies to improve overall survival. We design a reinforcement learning environment to model epithelial ovarian cancer treatment trajectories and use model free reinforcement learning to investigate therapeutic regimens for simulated patients.