Brett K. Beaulieu‐Jones

The paper examines how changes in control population definitions affect statistical results in a case-control study linking depression and Type 2 Diabetes Mellitus using real-world evidence.

The paper generates synthetic clinical data using deep neural networks with differential privacy to enable privacy-preserving data sharing for secondary analyses.

Deep learning rivals or beats baselines across imaging, health records, genomics and drug discovery but is not yet transformative; scarce labels, privacy and opacity remain hurdles.

The paper evaluates imputation methods for missing structured data in EHRs using clinical lab measures from 602,366 patients, showing some methods perform well while others are unsuitable for multiple imputation.

The paper learns patient trajectories from EHR data using deep learning methods and compares them to traditional machine learning approaches.