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The predictive intensive care unit (ICU) scoring system is crucial for predicting patient outcomes, particularly mortality. Traditional scoring systems rely mainly on structured clinical data from electronic health records, which can overlook important clinical information in narratives and images.

Demonstrate a methodology for improving discoverability of rare disease datasets by enriching source data with biological associations.

To enhance the accuracy of information retrieval from pharmacovigilance (PV) databases by employing Large Language Models (LLMs) to convert natural language queries (NLQs) into Structured Query Language (SQL) queries, leveraging a business context document.

This study evaluates the impact of an ambient artificial intelligence (AI) documentation platform on clinicians' perceptions of documentation workflow.

Digital health (patient portals, remote monitoring devices, video visits) is a routine part of health care, though the digital divide may affect access.

Deidentification of personally identifiable information in free-text clinical data is fundamental to making these data broadly available for research. However, there exist gaps in the deidentification landscape with regard to the functionality and flexibility of extant tools, as well as suboptimal tradeoffs between deidentification accuracy and speed. To address these gaps and tradeoffs, we develop a new Python-based deidentification software, pyDeid.

To compare various methods for extracting daily dosage information from prescription signatures (sigs) and identify the best performers.

In recent years, the rise of big data and artificial intelligence has led to an increasing expansion of databases and web services in biomedical research. cBioPortal is one of the most widely used platforms for accessing cancer genomic and clinical data. The primary objective of this study was to develop a tool that simplifies programmatic interaction with cBioPortal's web service.

To quantify utilization and impact on documentation time of a large language model-powered ambient artificial intelligence (AI) scribe.

This study evaluates the pilot implementation of ambient AI scribe technology to assess physician perspectives on usability and the impact on physician burden and burnout.