Resources


Database Credentialed Access

EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images

Seongsu Bae, Daeun Kyung, Jaehee Ryu, Eunbyeol Cho, Gyubok Lee, Sunjun Kweon, Jungwoo Oh, Lei JI, Eric Chang, Tackeun Kim, Edward Choi

We present EHRXQA, the first multi-modal EHR QA dataset combining structured patient records with aligned chest X-ray images. EHRXQA contains a comprehensive set of QA pairs covering image-related, table-related, and image+table-related questions.

question answering benchmark evaluation visual question answering electronic health records multi-modal question answering deep learning ehr question answering semantic parsing machine learning chest x-ray

Published: July 23, 2024. Version: 1.0.0

Database Credentialed Access

Tasks 1 and 3 from Progress Note Understanding Suite of Tasks: SOAP Note Tagging and Problem List Summarization

Yanjun Gao, John Caskey, Timothy Miller, Brihat Sharma, Matthew Churpek, Dmitriy Dligach, Majid Afshar

We introduce a hierarchical annotation suite of tasks addressing clinical text understanding, reasoning and abstraction over evidence, and diagnosis summarization. One task is section tagging major section and the other task is diagnosis generation.

Published: Sept. 30, 2022. Version: 1.0.0

Database Credentialed Access

Chest ImaGenome Dataset

Joy Wu, Nkechinyere Agu, Ismini Lourentzou, Arjun Sharma, Joseph Paguio, Jasper Seth Yao, Edward Christopher Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo Anthony Celi, Tanveer Syeda-Mahmood, Mehdi Moradi

The Chest ImaGenome dataset is a scene graph dataset with additional chronological comparison relations for chest X-rays. It is automatically derived from the MIMIC-CXR dataset. A manually annotated gold standard is also available for 500 patients.

scene graph visual dialogue object detection semantic reasoning bounding box knowledge graph explainability reasoning relation extraction chest cxr disease progression multimodal radiology visual question answering deep learning machine learning chest x-ray

Published: July 13, 2021. Version: 1.0.0

Database Credentialed Access

MS-CXR: Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing

Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel Coelho de Castro, Anton Schwaighofer, Stephanie Hyland, Harshita Sharma, Maria Teodora Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez Valle, Hoifung Poon, Ozan Oktay

MS-CXR is a new dataset containing 1162 chest X-ray bounding box labels paired with radiology text descriptions, annotated and verified by two board-certified radiologists.

vision-language processing localization phrase grounding chest x-ray

Published: Nov. 15, 2024. Version: 1.1.0

Database Credentialed Access

RadGraph2: Tracking Findings Over Time in Radiology Reports

Adam Dejl, Sameer Khanna, Patricia Therese Pile, Kibo Yoon, Steven QH Truong, Hanh Duong, Agustina Saenz, Pranav Rajpurkar

RadGraph2 is a dataset of 800 chest radiology reports annotated using a fine-grained entity-relationship schema, which captures key findings as well as mentions of changes that occurred in comparison with the previous radiology studies.

chest x-rays relation extraction disease progression information extraction radiology reports named entity recognition

Published: Aug. 8, 2024. Version: 1.0.0

Software Open Access

Software for computing Heart Rate Fragmentation

Madalena Costa

Heart rate fragmentation: a new method for the analysis of cardiac interbeat interval time series. The code provided can be run in Windows, Mac and Linux machines.

heart rate variability aging cardiovascular disease vagal tone time series analysis prediction of atrial fibrillation cardiac autonomic function prediction of cardiovascular events prediction of cognitive decline heart rate fragmentation

Published: Feb. 14, 2024. Version: 1.0.0

Database Credentialed Access

RadQA: A Question Answering Dataset to Improve Comprehension of Radiology Reports

Sarvesh Soni, Kirk Roberts

RadQA is an electronic health record question answering dataset containing clinical questions that can be answered using the Findings and Impressions sections of radiology reports

machine reading comprehension radiology reports question answering clinical notes electronic health records

Published: Dec. 9, 2022. Version: 1.0.0

Database Restricted Access

Hospitalized patients with heart failure: integrating electronic healthcare records and external outcome data

Zhongheng Zhang, Linghong Cao, Yan Zhao, Ziyin Xu, Rangui Chen, Lukai Lv, Ping Xu

The new version added beta blockers in the dat_md.csv file. Dataset comprising hospital-level data on patients who were admitted with heart failure to Zigong Fourth People’s Hospital, Sichuan, China between 2016 and 2019.

heart failure china electronic health record

Published: May 22, 2022. Version: 1.3

Database Credentialed Access

DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries

Jayetri Bardhan, Anthony Colas, Kirk Roberts, Daisy Zhe Wang

DrugEHRQA is a QA dataset containing question-answers from MIMIC-III tables and discharge summaries.

question-answer qa

Published: April 12, 2022. Version: 1.0.0

Database Credentialed Access

Chest ImaGenome Dataset

Joy Wu, Nkechinyere Agu, Ismini Lourentzou, Arjun Sharma, Joseph Paguio, Jasper Seth Yao, Edward Christopher Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo Anthony Celi, Tanveer Syeda-Mahmood, Mehdi Moradi

The Chest ImaGenome dataset is a scene graph dataset with additional chronological comparison relations for chest X-rays. It is automatically derived from the MIMIC-CXR dataset. A manually annotated gold standard is also available for 500 patients.

scene graph visual dialogue object detection semantic reasoning bounding box knowledge graph explainability reasoning relation extraction chest cxr disease progression multimodal radiology visual question answering deep learning machine learning chest x-ray

Published: July 13, 2021. Version: 1.0.0