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Using agile software development practices, develop and evaluate an architecture and implementation for reliable and user-friendly self-service management of bioinformatic data stored in the cloud.

Developing accurate phenotype definitions is critical in obtaining reliable and reproducible background rates in safety research. This study aims to illustrate the differences in background incidence rates by comparing definitions for a given outcome.

Anaphylaxis is a severe life-threatening allergic reaction, and its accurate identification in healthcare databases can harness the potential of "Big Data" for healthcare or public health purposes.

Statins are widely prescribed cholesterol-lowering medications in the United States, but their clinical benefits can be diminished by statin-associated muscle symptoms (SAMS), leading to discontinuation.

This study aimed to understand Black American women's attitudes toward seeking mental health services and using mobile technology to receive support for managing anxiety.

We evaluated autoencoders as a feature engineering and pretraining technique to improve major depressive disorder (MDD) prognostic risk prediction. Autoencoders can represent temporal feature relationships not identified by aggregate features. The predictive performance of autoencoders of multiple sequential structures was evaluated as feature engineering and pretraining strategies on an array of prediction tasks and compared to a restricted Boltzmann machine (RBM) and random forests as a benchmark.

We describe an automated transcription system that addresses many documentation problems and fits within scheduled clinical hours.

Present an artificial intelligence-enabled pipeline for estimating the prevalence of depression and general anxiety among pregnant women using texts from their social media posts. Use said pipeline to analyze mental health trends on subreddits frequented by pregnant women and report on interesting insights that could be helpful for policy-makers, clinicians, etc.

Remote monitoring of women experiencing hypertensive disorders of pregnancy (HDP) can provide timely life-saving data, particularly if these data are integrated into existing patient and clinical workflows. This pilot intervention of a smartphone application (app) for postpartum monitoring of hypertensive disorders integrates patient-contributed data into electronic health records (EHRs) to support monitoring and clinical decision-making. Results from the evaluation of the pilot highlight the resources needed when implementing the app [...]

Changes in short-acting beta-agonist (SABA) use are an important signal of asthma control and risk of asthma exacerbations. Inhaler sensors passively capture SABA use and may provide longitudinal data to identify at-riskpatients. We evaluate the performance of several ML models in predicting daily SABA use for participants with asthma and determine relevant features for predictive accuracy.