Resources

160 results for "mimic"

Model Credentialed Access

Clinical BERT Models Trained on Pseudo Re-identified MIMIC-III Notes

Eric Lehman, Sarthak Jain, Karl Pichotta, et al.

This project contains the weights of 7 different BERT models trained over a surrogate re-identified set of MIMIC-III (v1.4) notes. Each model is suitable for direct compatibility with the HuggingFace framework; users can easily load and apply the mo…
Published: April 28, 2021. Version: 1.0.0
Database Credentialed Access

National Institutes of Health Stroke Scale (NIHSS) Annotations for the MIMIC-III Database

Jiayang Wang, Xiaoshuo Huang, Lin Yang, et al.

The National Institutes of Health Stroke Scale (NIHSS) is a 15-item neurologic examination stroke scale. It quantifies the physical manifestations of neurological deficits and provides crucial support for clinical decision making and early-stage eme…
Published: Jan. 25, 2021. Version: 1.0.0
Database Credentialed Access

Annotated Question-Answer Pairs for Clinical Notes in the MIMIC-III Database

Xiang Yue, Xinliang Frederick Zhang, Huan Sun

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 …
Published: Jan. 15, 2021. Version: 1.0.0
Model Credentialed Access

Transformer models trained on MIMIC-III to generate synthetic patient notes

Ali Amin-Nejad, Julia Ive, Sumithra Velupillai

Natural Language Processing can help to unlock knowledge in the vast troves of unstructured clinical data that are collected during patient care. Patient confidentiality presents a barrier to the sharing and analysis of such data, however, meaning t…
Published: May 27, 2020. Version: 1.0.0
Database Credentialed Access

Phenotype Annotations for Patient Notes in the MIMIC-III Database

Edward Moseley, Leo Anthony Celi, Joy Wu, et al.

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 …
Published: March 5, 2020. Version: 1.20.3
Database Open Access

Clinical data from the MIMIC-II database for a case study on indwelling arterial catheters

Jesse Raffa

This dataset was created for the purpose of a case study in the book: Secondary Analysis of Electronic Health Records, published by Springer in 2016. In particular, the dataset was used throughout Chapter 16 (Data Analysis) by Raffa J. et al. to inv…
Published: Oct. 28, 2016. Version: 1.0
Database Open Access

MIMIC Database

The MIMIC Database includes data recorded from over 90 ICU patients. The data in each case include signals and periodic measurements obtained from a bedside monitor as well as clinical data obtained from the patient's medical record. The recordings …
Published: March 15, 2000. Version: 1.0.0 | Visualize waveforms
Database Restricted Access

MIMIC-III-Ext-Synthetic-Clinical-Trial-Questions

Elizabeth Woo, Michael Craig Burkhart, Emily Alsentzer, et al.

Large-language models (LLMs) show promise for extracting information from clinical notes. Deploying these models at scale can be challenging due to high computational costs, regulatory constraints, and privacy concerns. To address these challenges, …
Published: April 22, 2025. Version: 1.0.0
Database Credentialed Access

CAD-Chest: Comprehensive Annotation of Diseases based on MIMIC-CXR Radiology Report

Mengliang Zhang, Xinyue Hu, Lin Gu, et al.

Several extant chest X-ray (CXR) datasets predominantly comprise binary disease labels and exhibit a deficiency in providing comprehensive disease-related information. Crucial facets of disease management, including disease severity, diagnostic unce…
Published: Dec. 8, 2023. Version: 1.0
Database Credentialed Access

Medical Expert Annotations of Unsupported Facts in Doctor-Written and LLM-Generated Patient Summaries

Stefan Hegselmann, Shannon Shen, Florian Gierse, et al.

Large language models in healthcare can generate informative patient summaries while reducing the documentation workload of healthcare professionals. However, these models are prone to producing hallucinations, that is, generating unsupported inform…
Published: April 30, 2025. Version: 1.0.1