Year: 2025
Type: data-paper
Abstract: The EchoNotes Structured Database derived from MIMIC-III (ECHO-NOTE2NUM) is a structured echocardiogram database derived from 43,472 observational notes obtained during echocardiogram studies conducted in the intensive care unit at the Beth Israel Deaconess Medical Center between 2001 and 2012. The database encompasses various aspects of cardiac structure and function, including cavity size, wall thickness, systolic and diastolic function, valve regurgitation and stenosis, as well as pulmonary pressures. To facilitate extensive data analysis, the clinical notes were transformed into a structured numerical format. Within each echocardiogram report sentence, specific words or phrases were identified to describe abnormal findings, and a severity staging system using numeric categories was established. This large publicly accessible database of structured echocardiogram data holds significant potential as a tool to investigate cardiovascular disease in the intensive care unit and as a reference point for future note-based structured databases. Moreover, its structured nature allows for easy integration with other data types in MIMIC, such as images or vital signs, enabling large-scale data analysis and further advancements in this field.
Source: Scientific Data
Institutions: Harvard University, Emory University, Massachusetts General Hospital, Massachusetts Institute of Technology, Worcester Polytechnic Institute, United States Department of Veterans Affairs, Beth Israel Deaconess Medical Center, University of California, San Francisco, San Francisco VA Health Care System
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
Funders: National Science Foundation, U.S. Department of Health and Human Services, American Heart Association, Boettcher Foundation, Massachusetts Life Sciences Center, Korea Health Industry Development Institute, National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering, U.S. National Library of Medicine
Awards: G1198053936, A Learning Ecosystem for Teaching High School Students Machine Learning Concepts and Data Science Skills in Healthcare and Medicine, Bridge2AI: Patient-Focused Collaborative Hospital Repository Uniting Standards (CHoRUS) for Equitable AI, G4403456761, Research Resource for Complex Physiologic Signals, Critical Care Informatics, G5000979203, G5757594854, G5905476761, G7597440964, G7785066409, G8925663856, G9000013229, G9000218984
OpenAlex ID: W4412046766