Resources

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…

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 …

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…

Electronic Health Records (EHRs) combine structured data, such as medications and laboratory measurements, with unstructured clinical notes describing patient conditions and clinical events. Although these two data types provide complementary inform…

Clinical question answering (QA) (or reading comprehension) aims to automatically answer questions from medical professionals based on clinical texts. We release this dataset, which contains 1287 annotated QA pairs on 36 sampled discharge summaries …

Sharing patient data such as clinical notes among clinicians and researchers is fundamental to advancements in clinical practice and biomedical research. Prior to distribution, it is often necessary and legally mandated to remove identifiers such as…

MIMIC-III and MIMIC-IV are foundational resources for clinical NLP, but their scale is modest relative to the data volumes needed to train language models. Building on recent advances in synthetic data generation through LLM-based rephrasing, we con…

This dataset release provides medication extraction labels for a subset of 600 discharge summaries from the 2023 MIMIC-IV-Note dataset. These labels are consistent with the schema from the 2009 i2b2 Workshop on NLP Challenges dataset. We utilized a …

The development of large language models tailored for handling patients’ clinical notes is often hindered by the limited accessibility and usability of these notes due to strict privacy regulations. To address these challenges, we first create synth…

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…