Tools for the Precision Medicine Era: How to Develop Highly Personalized Treatment Recommendations From Cohort and Registry Data Using Q-Learning

By Elizabeth F. Krakow, Michael T. Hemmer, Tao Wang, Brent R. Logan, M. Arora, S. Spellman, D. Couriel, A. Alousi, J. Pidala, M. Last and 2 more. 2017

34 citations recorded by the source corpus.

Subfields: Statistics and Probability, Transplantation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Chao Yu
2. Jiming Liu
3. Shamim Nemati
4. Ning Liu
5. Jian Tang
6. Brent R. Logan
7. Yanzhi Wang
8. Zhiyuan Xu

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “lab tests in ICUs [222], [223], and prevention and treatments for graft versus host disease (GVHD) [224], [225] using data set from the Center for International Bone Marrow Transplant Research (CIBMTR) registry database.”
• Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data (2017, methodology): “Reference [22] adopted Q-learning [15], [23] to study a two-stage decision making problem in GVHD prevention and treatment, it simplified the problem of our motivating example to consider only two treatment options and two stages.”