Policy learning for clinical decision support
Alizée Pace (Ph.D. Student)
The increasing availability of large observational datasets of electronic health records give us the opportunity to address information management challenges facing modern clinicians. Machine learning solutions designed to optimise decision-making behaviour, such as reinforcement learning and treatment effects modelling, can be leveraged to provide recommendations for personalised treatment plans. From a methodological perspective, this work bridges the fields of RL and causal inference, with challenges such as learning entirely from purely observational data and from partially-observable representations, with potential sources of confounding. As an end-goal, I build on these methods to develop decision support frameworks for the intensive care.
|Primary Host:||Bernhard Schölkopf (ELLIS Institute Tübingen & Max Planck Institute for Intelligent Systems)|
|Exchange Host:||Gunnar Rätsch (ETH Zürich)|
|PhD Duration:||09 November 2021 - 09 November 2024|
|Exchange Duration:||01 September 2023 - 29 February 2024 - Ongoing|