Name
Abstract | ||
|---|---|---|
Building models of human decision-making from observed behaviour is critical to better understand, diagnose and support real-world policies such as clinical care. As established policy learning approaches remain focused on imitation performance, they fall short of explaining the demonstrated decision-making process. Policy Extraction through decision Trees (POETREE) is a novel framework for interpretable policy learning, compatible with fully-offline and partially-observable clinical decision environments -- and builds probabilistic tree policies determining physician actions based on patients' observations and medical history. Fully-differentiable tree architectures are grown incrementally during optimization to adapt their complexity to the modelling task, and learn a representation of patient history through recurrence, resulting in decision tree policies that adapt over time with patient information. This policy learning method outperforms the state-of-the-art on real and synthetic medical datasets, both in terms of understanding, quantifying and evaluating observed behaviour as well as in accurately replicating it -- with potential to improve future decision support systems. |
Year | Venue | Keywords |
|---|---|---|
2022 | International Conference on Learning Representations (ICLR) | Imitation Learning,Interpretable ML,Clinical Decision Support,Sequential Decision-Making |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alizée Pace | 1 | 0 | 0.34 |
| Alex J. Chan | 2 | 0 | 0.68 |
| Mihaela Van Der Schaar | 3 | 3968 | 352.59 |