Resources


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.

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

Published: July 13, 2021. 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

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

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

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, 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.

chest x-ray vision-language processing

Published: May 16, 2022. Version: 0.1

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.

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

Published: July 13, 2021. Version: 1.0.0