Resources

Oral diseases affect nearly 3.5 billion people, with the majority residing in low- and middle-income countries. Due to limited healthcare resources, many individuals are unable to access proper oral healthcare services. Image-based machine learning …

This dataset presents a curated collection of preprocessed and labeled clinical notes derived from the MIMIC-IV-Note database. The primary aim of this resource is to facilitate the development and training of machine learning models focused on summa…

In modern anesthesia, multiple medical devices are used simultaneously to comprehensively monitor real-time vital signs to optimize patient care and improve surgical outcomes. However, interpreting the dynamic changes of time-series biosignals and t…

Clinical risk prediction based on machine learning algorithms plays a vital role in modern healthcare. A crucial component in developing a reliable prediction model is a high-quality dataset with time series clinical events. In this work, we release…

The Paediatric Intensive Care (PIC) database is a large paediatric-specific, single-center, bilingual database comprising information relating to children admitted to critical care units at a large children’s hospital in China. The database is de-id…

MIMIC-CXR-DB is a relational clinical database comprising 146,333 chest radiographs linked to hospital admissions, designed to support research at the intersection of electronic health records (EHRs) and chest radiography. The database integrates ho…

Opioid use disorder (OUD) is underdiagnosed in health system settings, limiting research on OUD using electronic health records (EHRs). Medical encounter notes can enrich structured EHR data with documented signs and symptoms of OUD and social risks…

This dataset encompasses a comprehensive collection of 2,321 chest radiographs (originally DICOM, converted to PNG) and 11,072 smartphone images documenting various cardiac implantable electronic devices (CIEDs) such as implantable pacemakers, cardi…

This dataset is a curated subset of MIMIC-III (v1.4), specifically formatted to facilitate reproducibility of the experiments in the work t-PatchGNN. It serves as part of a benchmark designed for forecasting irregular multivariate clinical time seri…

This is a preprocessed dataset derived from patient records in MIMIC-III and eICU, two large-scale electronic health record (EHR) databases. It contains features and labels for 5 prediction tasks involving 3 adverse outcomes (prediction times listed…