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Machine Learning for Informed Clinical Decision Making
QI: MIT Quest for Intelligence
Spring - February 7, 2020 Summer - April 10, 2020
We are seeking highly-motivated students to participate in research to develop machine learning (ML) techniques for informed sequential clinical treatment decision making. This project will develop ML approaches for personalized counterfactual predictions and learning dynamic treatment regimes for time-varying treatments using both simulated data and observational data from MIMIC III, a large intensive care unit (ICU) database.
Knowledge and experience in machine learning would be required. Experience in one or more of the following areas would be highly desirable: deep learning, representation learning, reinforcement learning, Bayesian probabilistic models (e.g. Gaussian Processes). Knowledge in causal inference is a plus, but not required. Language: Python. Experience with PyTorch would be desirable. Interested applicants should send their resumes via email.