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To describe the development and implementation of an automated platform for genomic risk prediction that integrates multiple data types.

Atrial fibrillation (AF) is a common arrhythmia affecting a large fraction of patients in intensive care units (ICUs). Predicting patients at risk of AF in the ICU is a challenging task and is not commonly practiced but could have clinical implications considering that AF is a proxy for poorer outcomes. Our goal is to develop a real-time AF prediction model using ICU numerical data to improve alarm quality, helping clinicians [...]

To develop a pipeline for evaluating large language models (LLMs) on the task of capturing symptoms from clinical encounters.

To systematically map co-occurrence patterns between systemic medical conditions and oral diseases in the biomedical literature.

Develop and validate subtype-specific, fairness-aware machine learning (ML) models that integrate clinical and social determinants of health (SDoH) information to predict 6-month readmission or mortality after hospitalization among patients with HFpEF or HFrEF, and to evaluate the incremental contribution of SDoH, model explainability, and subgroup error disparities across demographic groups.

To analyze oncology nurses' accounts of technology-related safety events and the sociotechnical factors contributing to patient-, workforce-, and system-level harms.

Implementing target trial emulation (TTE) studies as standardized, reproducible analytic workflows is technically demanding. We developed Text-guided Health-study Estimation and Specification Engine Using Strategus (THESEUS), which uses large language models (LLMs) to translate free-text study descriptions into structured analytic specifications and Strategus R scripts within the Observational Health Data Sciences and Informatics (OHDSI) ecosystem.

Clinicians' adoption of interoperability tools influences care quality, but evidence of actual use is limited. We analyze clinicians' use of outside records delivered via Epic Care Everywhere (CE), focusing on use frequency and predictors such as gender, experience, specialty, and role. Differences between pre-pandemic (2018-2019) and pandemic (2020-2021) periods are also examined to see how COVID-19 affected use of outside records.

Develop a near-comprehensive opioid medications valueset for population measures of opioid related treatments and outcomes. The opioid valueset should be free, open source, and conform to the RxNorm standard federally mandated in every US-certified electronic health record.

This study aims to develop comprehensive computable phenotyping algorithms that integrate multiple domains of electronic health record (EHR) data within the All of Us (AoU) Researcher Workbench to identify sexually transmitted infection (STI) cases and characterize STI patterns.