Resources

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 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…

Large language models (LLMs) have recently demonstrated remarkable capabilities across a broad spectrum of tasks and applications, including the medical field. Models like GPT-4 excel in medical question answering but encounter challenges in interpr…

Measurable progress in the development of medical decision support systems has been hindered by a lack of comprehensive datasets. Many available datasets focus on narrow prediction tasks and do not include a diverse range of data types, which limits…

We introduce a dataset of people’s names and clinical note templates to support research on the demographic bias of de-identification systems. The dataset contains 16 name sets that vary along four demographic dimensions: gender, race, name populari…