ABSTRACT

This project introduces a framework for interpretable, patient-centered causal inference and policy design for in-hospital patient care. This framework arose from a challenging problem, which is how to treat critically ill patients who are at risk for seizures (subclinical seizures) that can severely damage a patient's brain. In this high-stakes application of artificial intelligence, the data are complex, including noisy time-series, medical history, and demographic information. The goal is to produce interpretable causal estimates and policy decisions, allowing doctors to understand exactly how data were combined, permitting better troubleshooting, uncertainty quantification, and ultimately, trust. The core of the project's framework consists of novel and sophisticated matching techniques, which match each treated patient in the dataset with other (similar) patients who were not treated. Matching emulates a randomized controlled trial, allowing the effect of the treatment to be estimated for each patient, based on the outcomes from their matched group. A second important element of the framework involves interpretable policy design, where sparse decision trees will be used to identify interpretable subgroups of individuals who should receive similar treatments.


The matching techniques developed in this project will be within the new family of "almost-matching-exactly" (AME) techniques. AME techniques use machine learning on a training set to determine how to construct high-quality matched groups. In applying AME techniques to analyze seizure risk and treatment for critically ill patients, there are two major challenges that this project addresses: how to incorporate mechanistic models for drug absorption, and how to perform uncertainty quantification. Importantly, the project also addresses the release of AME code in several formats to be used by non-experts. The policy design aspect of the project involves the optimization of sparse decision trees. This project involves a close collaboration between experts in machine learning, causal inference, databases, and neurology, with the goal to improve patient care in high-stakes hospital settings where experiments cannot be conducted, and the only way to assess causal effects is through the analysis of observational data.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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