Resources

This dataset contains de-identified encounter level data from a 1-year, multisite, pragmatic, cluster-randomized controlled trial evaluating the CONCERN (COmmunicating Narrative Concerns Entered by RNs) early warning system, a machine learning-based…

The ER-Reason dataset is a benchmark designed to evaluate LLM-based clinical reasoning and decision-making in the emergency room (ER), a high-stakes setting where clinicians make rapid, consequential decisions across diverse patient presentations an…

The CARMEN-I corpus comprises 2,000 clinical records, encompassing discharge letters, referrals, and radiology reports from Hospital Clínic of Barcelona between March 2020 and March 2022. These reports, primarily in Spanish with some Catalan section…

Unlocking the full potential of clinical notes within electronic health records presents a challenge for large scale, computational analyses. Many studies that incorporate information from clinical notes do so with simple word-matching approaches an…

MIMIC is one of the most widely used public hospital datasets for machine learning on electronic health records [3], but its structured tables answer many clinical questions only through coded surrogates, or not at all. For example, a patient's past…

Bedside monitors in intensive care units continuously generate high-resolution physiological signals that are rarely preserved systematically for research. This resource makes available to the community a set of multiparametric waveform recordings p…

This is the pilot year of the ShARe/CLEF eHealth Evaluation Lab, a shared task focused on natural language processing (NLP) and information retrieval (IR) for clinical care. The task is co-organized by the Shared Annotated Resources (ShARe) project …

MIMIC-IV-Ext-CLIF is a derived dataset of MIMIC-IV v3.1, transformed into the Common Longitudinal ICU data Format (CLIF). CLIF is an open-source critical care data model developed by a consortium of more than 10 US academic medical centers to standa…

Applying methods in natural language processing on electronic health records (EHR) data is a growing field. Existing corpus and annotation focus on modelling textual features and relation prediction [1] . However, there is a paucity of annotated cor…

RadCoref is a small subset of MIMIC-CXR with manually annotated coreference mentions and clusters. The dataset is annotated by a panel of three cross-disciplinary experts with experience in clinical data processing following the i2b2 annotation sche…