Resources

A crucial step within secondary analysis of electronic health records (EHRs) is to identify the patient cohort under investigation. While EHRs contain medical billing codes that aim to represent the conditions and treatments patients may have, much …

MIMIC-III is a large, freely-available database comprising deidentified health-related data associated with over 40,000 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012 [1]. The MIMIC-III C…

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…

This dataset provides clinical Named Entity Recognition (NER) and Assertion Classification (AC) annotations for 400 discharge summaries from MIMIC-IV-Note (v2.2), designed to support research on computer-assisted ICD coding and related clinical natu…

The MIMIC-IV-Echo module contains structured echocardiographic measurements and DICOM files from echocardiography exams. Structured measurements are available from 206,488 echocardiogram studies (including 179,928 transthoracic, 16,389 stress, and 1…

This dataset was designed and created to enable advancements in healthcare-focused large language models, particularly in the context of retrieval-augmented clinical question-answering capabilities. Developed using a self-constructed pipeline based …

This challenge, sponsored by SNOMED International, seeks to advance the development of Entity Linking models that operate on unstructured clinical texts. Participants in the challenge will train entity linking models using a subset of MIMIC-IV-Note …

MIMIC-III is a large, freely-available database comprising deidentified health-related data associated with over forty thousand patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. The databas…

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…

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…