Data Science Seminar
Towards Reinforcement Learning for Precision Drug Dosing
Sumana Basu
Towards Reinforcement Learning for Precision Drug Dosing
| When | Wednesday, April 19, 2023, 10:45 AM – 11:45 AM (MT) |
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Abstract
Drug dosing is an important application of AI, which can be formulated as a Reinforcement Learning (RL) problem, since every individual’s drug dosing requirement is different. In this talk, we will talk about two major challenges of using RL for drug dosing: delayed and prolonged effects of medications, which break the Markov assumption of the RL framework. We will talk about an approach to solve this problem in a model free reinforcement learning setting, talk further about the challenges of deploying it in real life and sketch the outline of a more realistic Model Based Reinforcement Learning (MBRL) solution to it.
Speaker
Sumana Basu
McGill University
Sumana is a PhD student at McGill University (Mila), researching Deep Reinforcement Learning in Healthcare. Her work focuses on applying Reinforcement Learning to Autonomous Drug Dosing. She completed her Masters at Mila, where she studied deep learning for predicting Alzheimer’s disease progression. She has also interned in the past at Meta AI (FAIR) on the fastMRI Active Acquisition project, using Reinforcement Learning to accelerate MRI acquisition, and will be interning at Microsoft Research Labs coming summer, on exploring Reinforcement Learning for optimizing genetic perturbation in Cancer Treatment.
Tags: health & medicine machine learning
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