Improving management of Anemia in End Stage Renal Disease using Reinforcement Learning

By Adam E. Gaweda. 2009

9 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Hematology, Management of Technology and Innovation

How researchers see this paper

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

In Artificial Intelligence

1. Chao Yu
2. Jiming Liu
3. Shamim Nemati
4. Jordan M. Malof
5. Adam E. Gaweda
6. Guoqi Ren

In Hematology

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu
4. Jordan M. Malof

In Management of Technology and Innovation

1. Shamim Nemati
2. Chao Yu
3. Jiming Liu
4. Jordan M. Malof

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, background): “able anemia management than the existing AMP, an approach that incorporates a critical prior knowledge about the dose-response characteristic into the learning process has been proposed [138], [139], [140]. This is driven by the fact that, for all patients, the dose-response curve of HGB versus EPO ”
• Optimizing drug therapy with Reinforcement Learning: The case of Anemia Management (2011, background): “problem and show that they can be used to derive Epo dosing strategies that match or outperform a standard AMP [ 6 ].”