Resources

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.

A SaO2-SpO2 Pairs Dataset derived from MIMIC-IV

Dataset of questions asked by medical experts about patients. Medical experts will read a discharge summary line-by-line and (1) ask any question that they may have and (2) record what in the text "triggered" them to ask their question.