Resources

Clinical risk prediction based on machine learning algorithms plays a vital role in modern healthcare. A crucial component in developing a reliable prediction model is a high-quality dataset with time series clinical events. In this work, we release…

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…

This dataset is an automated ETL conversion of the MIMIC-IV Clinical Database Demo into the Medical Event Data Standard (MEDS). MEDS is a data schema for storing streams of medical events such as those sourced from Electronic Health Records or claim…

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 …

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…

We present MIMICEchoQA, a benchmark dataset for echocardiogram-based question answering, built from the publicly available MIMIC-IV-ECHO database. Each echocardiographic study was paired with the closest discharge summary within a 7-day window, and …

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 comprises 9,606 Synthetic Discharge Summaries generated by GPT-3.5 based on combinations of ICD-10-code descriptions associated with real discharge summaries in MIMIC-IV. As part of the generation process, GPT-3.5 was also tasked to cod…

Pulmonary embolism (PE) is a leading cause of preventable in-hospital mortality. Advances in diagnosis, risk stratification, and prevention can improve outcomes. Large, publicly available datasets are needed to move research forward but are lacking …

Medical concept extraction from electronic health records underpins many downstream applications, yet remains challenging because medically meaningful concepts, such as diagnosis, are frequently implied rather than explicitly stated in medical narra…