Resources

RadGraph is a dataset of entities and relations in full-text radiology reports. We designed a novel information extraction (IE) schema to structure clinical information in a radiology report with four entities and three relations. Our train set cons…

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 …

CXRGraph is a dataset of structured radiology reports dataset following the RadGraph format, which has been tailored for the Automatic Radiology Report Generation (ARRG) task. CXRGraph assorts clinical information from full-text radiology reports in…

Both medical care and observational studies in oncology require a thorough understanding of a patient's disease progression and treatment history, often elaborately documented within clinical notes. As large language models (LLMs) are becoming more …

Large-language models (LLMs) show promise for extracting information from clinical notes. Deploying these models at scale can be challenging due to high computational costs, regulatory constraints, and privacy concerns. To address these challenges, …

Large-language models (LLMs) show promise for extracting information from clinical notes. Deploying these models at scale can be challenging due to high computational costs, regulatory constraints, and privacy concerns. To address these challenges, …

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 study presents MIMIC-III-Ext-SBDH, a manually reviewed and consolidated annotation dataset of social and behavioral determinants of health (SBDH) derived from clinical notes in the MIMIC-III database. We previously developed SBDH-Reader, a larg…

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…

Named Entity Recognition (NER) and Entity Normalization (EN) are fundamental tasks in information extraction, particularly in the biomedical and clinical domains. NER identifies textual mentions of entities, while EN maps these mentions to unique id…