Tree-based reinforcement learning for estimating optimal dynamic treatment regimes
| Autor: | Yebin Tao, Lu Wang, Daniel Almirall |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Mathematical optimization Computer science Decision tree Multi-stage decision-making Estimator 02 engineering and technology personalized medicine 01 natural sciences Article 010104 statistics & probability Tree (data structure) Identification (information) Inverse probability classification backward induction Modeling and Simulation Backward induction decision tree 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing Semiparametric regression 0101 mathematics Statistics Probability and Uncertainty |
| Zdroj: | Ann. Appl. Stat. 12, no. 3 (2018), 1914-1938 |
| Popis: | Dynamic treatment regimes (DTRs) are sequences of treatment decision rules, in which treatment may be adapted over time in response to the changing course of an individual. Motivated by the substance use disorder (SUD) study, we propose a tree-based reinforcement learning (T-RL) method to directly estimate optimal DTRs in a multi-stage multi-treatment setting. At each stage, T-RL builds an unsupervised decision tree that directly handles the problem of optimization with multiple treatment comparisons, through a purity measure constructed with augmented inverse probability weighted estimators. For the multiple stages, the algorithm is implemented recursively using backward induction. By combining semiparametric regression with flexible tree-based learning, T-RL is robust, efficient and easy to interpret for the identification of optimal DTRs, as shown in the simulation studies. With the proposed method, we identify dynamic SUD treatment regimes for adolescents. |
| Databáze: | OpenAIRE |
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