MIMIC-III-Ext-Notes

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 MIMIC-III-Ext-Notes dataset was developed to address
this gap by providing a resource for evaluating large language models (LLMs)
and other natural language processing (NLP) systems in extracting and
contextualizing clinical information.

The dataset includes 150 clinical notes randomly sampled from the MIMIC-III
Clinical Database, from which 2,288 clinical concepts were identified using
MetaMap and annotated by clinicians for detection accuracy, encounter
relevance, and negation status. The resulting dataset enables the evaluation
of models' abilities to recognize, interpret, and reason about symptom
mentions and disease concepts within realistic clinical narratives. By
incorporating both concept-level and contextual annotations, MIMIC-III-Ext-
Notes provides a valuable benchmark for developing, testing, and validating
NLP and LLM frameworks designed for clinical text understanding and decision
support applications.