Buch ; Online: On the Challenges of using Reinforcement Learning in Precision Drug Dosing
Delay and Prolongedness of Action Effects
2023
Abstract: Drug dosing is an important application of AI, which can be formulated as a Reinforcement Learning (RL) problem. In this paper, we identify two major challenges of using RL for drug dosing: delayed and prolonged effects of administering medications, ... ...
| Abstract | Drug dosing is an important application of AI, which can be formulated as a Reinforcement Learning (RL) problem. In this paper, we identify two major challenges of using RL for drug dosing: delayed and prolonged effects of administering medications, which break the Markov assumption of the RL framework. We focus on prolongedness and define PAE-POMDP (Prolonged Action Effect-Partially Observable Markov Decision Process), a subclass of POMDPs in which the Markov assumption does not hold specifically due to prolonged effects of actions. Motivated by the pharmacology literature, we propose a simple and effective approach to converting drug dosing PAE-POMDPs into MDPs, enabling the use of the existing RL algorithms to solve such problems. We validate the proposed approach on a toy task, and a challenging glucose control task, for which we devise a clinically-inspired reward function. Our results demonstrate that: (1) the proposed method to restore the Markov assumption leads to significant improvements over a vanilla baseline; (2) the approach is competitive with recurrent policies which may inherently capture the prolonged effect of actions; (3) it is remarkably more time and memory efficient than the recurrent baseline and hence more suitable for real-time dosing control systems; and (4) it exhibits favorable qualitative behavior in our policy analysis. Comment: Accepted to AAAI 2023 |
|---|---|
| Schlagwörter | Computer Science - Machine Learning |
| Thema/Rubrik (Code) | 006 |
| Erscheinungsdatum | 2023-01-01 |
| Erscheinungsland | us |
| Dokumenttyp | Buch ; Online |
| Datenquelle | BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl) |
Volltext online
Zusatzmaterialien
Kategorien
Fernleihe an ZB MED
Sie können sich den gewünschten Titel als lokale Nutzerin oder lokaler Nutzer von ZB MED direkt an den Standort Köln schicken lassen.