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Estimating What-if Outcomes for Targeting Interventions in a Clinical Setting
Suchi Saria

Individuals have heterogeneous outcomes from interventions. In a clinical setting, estimating how patients will respond to different treatments is critical for targeted care. Clinicians constantly ask themselves, given a patient’s history, what would happen to the patient’s clinical trajectory if they were given one treatment versus another. However, in practice it is often unknown how the patient’s signals will change in response to treatment until that treatment is actually administered. Even then, it is impossible to observe the counterfactual from real data, i.e., what would have happened to the patient if the doctor had made a different choice. In order to solve this causal question, we use the g-formula with proper assumptions to estimate physiologic trajectories and treatment responses from observed data. To demonstrate this we model blood pressure and heart rate for patients in the intensive care unit (ICU) and estimate their responses to six types of treatments that are used in their management. These two signals are among the most commonly used vital signs in the ICU and are critical for identifying life-threatening conditions like septic and hemorrhagic shock. To model the signal with treatment response from observed data, we use two different Bayesian non-parametric (BNP) methods to build the estimator. BNP are known to have an extremely flexible functional form, which helps to overcome the model mis-specification problem and makes the estimator more robust.

Author Information

Suchi Saria (Johns Hopkins University)

Suchi Saria is an assistant professor of computer science, health policy and statistics at Johns Hopkins University. Her research interests are in statistical machine learning and computational healthcare. Specifically, her focus is in designing novel data-driven computing tools for optimizing decision-making. Her work is being used to drive electronic surveillance for reducing adverse events in the inpatient setting and individualize disease management in chronic diseases. She received her PhD from Stanford University with Prof. Daphne Koller. Her work has received recognition in the form of two cover articles in Science Translational Medicine (2010, 2015), paper awards by the the Association for Uncertainty in Artificial Intelligence (2007) and the American Medical Informatics Association (2011), an Annual Scientific Award by the Society of Critical Care Medicine (2014), a Rambus Fellowship (2004-2010), an NSF Computing Innovation fellowship (2011), and competitive awards from the Gordon and Betty Moore Foundation (2013), and Google Research (2014). In 2015, she was selected by the IEEE Intelligent Systems to the ``AI's 10 to Watch'' list. In 2016, she was selected as a DARPA Young Faculty awardee and to Popular Science's ``Brilliant 10’’.

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