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We survey clinical document corpora, with a focus on German textual data. Due to rigid data privacy legislation in Germany, these resources, with only few exceptions, are stored in protected clinical data spaces and locked against clinic-external researchers. This situation stands in stark contrast with established workflows in the field of natural language processing, where easy accessibility and reuse of (textual) data collections are common practice. Hence, alternative corpus designs [...]

This work evaluated algorithmic bias in biomarkers classification using electronic pathology reports from female breast cancer cases. Bias was assessed across 5 subgroups: cancer registry, race, Hispanic ethnicity, age at diagnosis, and socioeconomic status.

To describe challenges and solutions for calculating longitudinal daily opioid dose in morphine milligram equivalents from electronic health record prescriptions for a clinical trial of voluntary opioid reduction in patients with chronic non-cancer pain.

This study aimed to systematically evaluate and compare the diagnostic performance of leading large language models (LLMs) in common and complex clinical scenarios, assessing their potential for enhancing clinical reasoning and diagnostic accuracy in authentic clinical decision-making processes.

This study aims to explore multimorbidity patterns associated with diabetes by analyzing user engagement in online diabetes support communities and their interactions with other disease-related communities. Additionally, it seeks to assess whether early signals of diabetes can be detected through online engagement data.

The main goal is to develop an automated process for connecting Research Electronic Data Capture (REDCap) instances in a clinical trial network to allow for deidentified transfer of research surveys to cloud computing data commons for discovery.

This work aims to provide a specialized template for integrating diverse study data types with varying sharing restrictions specific to NIH HEAL Initiative chronic pain and substance use research.

Congenital heart disease (CHD) patients with single ventricle physiology (SVP) have heterogeneous characteristics that challenge cohort classification. We aim to develop a phenotyping algorithm that accurately identifies SVP patients using electronic health record (EHR) data.

Data-sharing policies are rapidly evolving toward increased data sharing. However, participants' perspectives are not well understood and could have an adverse impact on participation in research. We evaluated participants' preferences for sharing specific types of data with specific groups, and strategies to enhance trust in data-sharing practices.

We used clinical decision support (CDS) to promote compliance with the 21st Century Cures Act's mandate that, with few exceptions, patients be granted timely access to their clinical notes.