Resources

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 …

Recent advances in scaling large language models (LLMs) has resulted in significant improvements over a number of natural language processing benchmarks. There has been some work to pretrain these language models over clinical text. These works demo…

The advent of large, open access text databases has driven advances in state-of-the-art model performance in natural language processing (NLP). The relatively limited amount of clinical data available for NLP has been cited as a significant barrier …

Radiology reports are essential for clinical care but pose challenges for automated processing due to their unstructured nature. Existing datasets like RadGraph-1.0 focus narrowly on chest X-rays (CXR), limiting their applicability. We introduce Rad…

This project focuses on Task 2 of the 2014 ShARe/CLEF eHealth evaluation lab, which extended Task 1 of the 2013 ShARe/CLEF eHealth evaluation lab by focusing on identification and normalization of disorder attributes. The task comprises two subtasks…

SemEval (Semantic Evaluation) is an ongoing series of evaluations of computational semantic analysis systems, organized under the umbrella of SIGLEX, the Special Interest Group on the Lexicon of the Association for Computational Linguistics. This pr…

Unstructured clinical documentation, such as progress notes, contains rich contextual information critical for clinical decision-making but remains underutilized in computational research due to the limited availability of annotated datasets. The MI…

This database supports research on stigmatizing language in clinical documentation concerning gender-expansive patients. It contains expert-reviewed, note-level annotations for 754 deidentified discharge summaries from MIMIC-IV-Note, including 366 n…

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, Swiss-Mammo, contains 28 manually constructed German mammography reports, each paired with an English translation. The reports are stratified across BI-RADS categories 0 through 6, with four reports per category. All reports were manua…