Resources

A crucial step within secondary analysis of electronic health records (EHRs) is to identify the patient cohort under investigation. While EHRs contain medical billing codes that aim to represent the conditions and treatments patients may have, much …

Radiology reports are essential for clinical care but pose challenges for automated processing due to their unstructured nature. Existing datasets like RadGraph-1.0 focus narrowly on chest X-rays (CXR), limiting their applicability. We introduce Rad…

Brno University of Technology ECG Signal Database with Annotations of P Wave (BUT PDB) is an ECG signal database with marked peaks of P waves created by the cardiology team at the Department of Biomedical Engineering, Brno University of Technology. …

This database supports research on stigmatizing language in clinical documentation concerning gender-expansive patients. It contains expert-reviewed, note-level annotations for 754 deidentified discharge summaries from MIMIC-IV-Note, including 366 n…

The analysis and identification of abnormal ventricular potentials (AVPs) in intracardiac electrograms (EGMs) are paramount in radiofrequency catheter ablation (RFCA) of post-ischemic ventricular tachycardia (VT). The ARGO dataset represents the fir…

The development of robust echocardiography analyses has been limited by the scarcity of well-annotated, labeled echocardiogram datasets. We introduce the Structured Viewing Classification Annotations From the MIMIC-IV-ECHO Dataset (ECHOVIEW), a deri…

Glaucomatous optic neuropathy (GON) is a leading cause of irreversible blindness worldwide, affecting an estimated 64.3 million people globally with projections reaching 111.8 million by 2040. Approximately 50% of cases remain undiagnosed until adva…

This project presents an annotated dataset derived from MIMIC-III and MIMIC-IV discharge summaries, focusing on key Social Determinants of Health (SDoH) factors—social support, occupation, and substance use—and their association with adverse pregnan…

Large language models in healthcare can generate informative patient summaries while reducing the documentation workload of healthcare professionals. However, these models are prone to producing hallucinations, that is, generating unsupported inform…

Clinical question answering (QA) (or reading comprehension) aims to automatically answer questions from medical professionals based on clinical texts. We release this dataset, which contains 1287 annotated QA pairs on 36 sampled discharge summaries …