Contextual Bandits with Budgeted Information Reveal.

Contextual bandit algorithms are commonly used in digital health to recommend personalized treatments. However, to ensure the effectiveness of the treatments, patients are often requested to take actions that have no immediate benefit to them, which we refer to as actions. In practice, clinicians have a limited budget to encourage patients to take these actions and collect additional information.

Kernel Debiased Plug-in Estimation: Simultaneous, Automated Debiasing without Influence Functions for Many Target Parameters.

When estimating target parameters in nonparametric models with nuisance parameters, substituting the unknown nuisances with nonparametric estimators can introduce "plug-in bias." Traditional methods addressing this suboptimal bias-variance trade-off rely on the (IF) of the target parameter. When estimating multiple target parameters, these methods require debiasing the nuisance parameter multiple times using the corresponding IFs, which poses analytical and computational challenges.

Awarding additional MELD points to the shortest waitlist candidates improves sex disparity in access to liver transplant in the United States.

Since the introduction of the MELD-based allocation system, women are now 30% less likely than men to undergo liver transplant (LT) and have 20% higher waitlist mortality. These disparities are in large part due to height differences in men and women though no national policies have been implemented to reduce sex disparities. Patients were identified using the Scientific Registry of Transplant Recipients (SRTR) from 2014 to 2019.

Machine-learning algorithms for predicting hospital re-admissions in sickle cell disease.

Reducing preventable hospital re-admissions in Sickle Cell Disease (SCD) could potentially improve outcomes and decrease healthcare costs. In a retrospective study of electronic health records, we hypothesized Machine-Learning (ML) algorithms may outperform standard re-admission scoring systems (LACE and HOSPITAL indices). Participants (n = 446) included patients with SCD with at least one unplanned inpatient encounter between January 1, 2013, and November 1, 2018.