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Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks
Bryan Lim · Ahmed Alaa · Mihaela van der Schaar

Wed Dec 05 02:00 PM -- 04:00 PM (PST) @ Room 210 #63

Electronic health records provide a rich source of data for machine learning methods to learn dynamic treatment responses over time. However, any direct estimation is hampered by the presence of time-dependent confounding, where actions taken are dependent on time-varying variables related to the outcome of interest. Drawing inspiration from marginal structural models, a class of methods in epidemiology which use propensity weighting to adjust for time-dependent confounders, we introduce the Recurrent Marginal Structural Network - a sequence-to-sequence architecture for forecasting a patient's expected response to a series of planned treatments. Using simulations of a state-of-the-art pharmacokinetic-pharmacodynamic (PK-PD) model of tumor growth, we demonstrate the ability of our network to accurately learn unbiased treatment responses from observational data – even under changes in the policy of treatment assignments – and performance gains over benchmarks.

Author Information

Bryan Lim (University of Oxford)
Ahmed Alaa (UCLA)
Mihaela van der Schaar (UCLA and Oxford University)

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