Current clinical guidelines for hypertension provide physicians with general suggestions for first-line pharmacologic treatment, but do not consider patient-specific characteristics. We utilize electronic health record data to develop personalized predictions and prescription models for hypertensive patients. We demonstrate a 15.87% improvement over the standard of care and propose a novel interactive dashboard to facilitate the deployment of the derived models in the clinical practice.

Rapid and accurate detection of local outbreaks is critical to tackle resurgent waves of COVID-19. A fundamental challenge in case growth rate estimation, a key epidemiological parameter, is balancing the accuracy vs. speed tradeoff for small sample sizes of counties. We present “Transfer Learning for Generalized Random Forests” (TLGRF), a novel framework which uses relevant features affecting the disease spread across time and counties to obtain more robust and timelier county-level estimates.

We address a set of problems that occur in the development of liquid biopsies via the lens of active sequential hypothesis testing (ASHT). Motivated by applications in which the number of hypotheses or actions is massive, we propose efficient algorithms and provide the first approximation guarantees for ASHT, under two types of adaptivity. We numerically evaluate the performance of our algorithms using both synthetic and real-world DNA mutation data.