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.

The DFCI-imaging-student and DFCI-medonc-student AI models for extracting cancer outcomes from imaging reports and medical oncologist notes from electronic health records.

Two sets of gold-standard annotations for race and ethnicity information from clinical notes in MIMIC-III. Contains race and ethnicity label assignments and related information such as country of origin and spoken language.

Annotations of ten types of medical decisions from discharge summaries in the MIMIC-III database.

A new dental dataset that contains 169 patients, three commonly used dental image models, and images of various health conditions of the oral cavity.

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

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.

MIMIC-IV-ext-MDS-ED proposes a dataset to benchmark multimodal decision support in the emergency department. It features multimodal input (including ECG waveforms) and a comprehensive set of prediction targets (diagnoses and deterioration prediction)

The SICdb dataset, version 1.0.8 contains 27350 admissions to an ICU in an Austrian tertiary care institution.

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.