Resources

We present the Synthetic Mention Corpora for Disease Entity Recognition and Normalization, containing 128000 disease mentions from the UMLS disorder group, generated by an LLM. This corpus aims to improve these tasks in biomedical and clinical texts.

CGMacros contains information from two continuous glucose monitors (CGM), food macronutrients, food photographs, physical activity, and anonymized participant demographics, anthropometric measurements and health parameters.

COVID Data for Shared Learning (CDSL) is a multimodal database comprising de-identified structured health data and radiological images from 4,479 patients with COVID-19, as a comprehensive toolkit for developing predictive models.

Large database of de-identified health information from patients admitted to Beth Israel Deaconess Medical Center

VTaC is an annotated ventricular tachycardia (VT) arrhythmia alarm database containing over 5,000 waveform recordings with VT alarms from ICU monitors, with each alarm labeled as either true or false by at least two human expert annotators.

This dataset includes polysomnographic sleep recordings from a study on sleep-related arousal diagnostics, featuring raw and derived data channels, annotated event types, and questionnaire data.

Dataset that links ECG records from MIMIC-IV-ECG to ED discharge and hospital discharge diagnoses, which enables to train general ECG prediction models based on clinical labels and facilitates the retrieval of further clinical metadata from MIMIC-IV.

We present EHRXQA, the first multi-modal EHR QA dataset combining structured patient records with aligned chest X-ray images. EHRXQA contains a comprehensive set of QA pairs covering image-related, table-related, and image+table-related questions.

Chest radiographs in DICOM format with associated free-text reports.

We introduce MIMIC-Ext-MIMIC-CXR-VQA, a complex, diverse, and large-scale dataset designed for Visual Question Answering (VQA) tasks within the medical domain, focusing primarily on chest radiographs.