An Ontology for Cardiothoracic Surgical Education and Clinical Data Analytics

Author(s)

Maryam Panahiazar, Bakar Computational Health Sciences Institute, School of Medicine, University of California San Francisco, San Francisco, CA, USA
Yorick Chern, Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA
Ramon Riojas, Department of Surgery, Division of Cardiothoracic Surgery, School of Medicine, University of California San Francisco, San Francisco, CA, USA
Omar S.Latif, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA
Usha Lokala, University of South Carolina - ColumbiaFollow
Dexter Hadley, Department of Clinical Sciences, College of Medicine, University of Central Florida, Orlando, FL, USA
Amit Sheth, University of South Carolina - ColumbiaFollow
Ramin E.Beygui, Department of Surgery, Division of Cardiothoracic Surgery, School of Medicine, University of California San Francisco, San Francisco, CA, USA

Document Type

Book Chapter

Subject Area(s)

Studies in Health Technology and Informatics

Abstract

The development of an ontology facilitates the organization of the variety of concepts used to describe different terms in different resources. The proposed ontology will facilitate the study of cardiothoracic surgical education and data analytics in electronic medical records (EMR) with the standard vocabulary.

Digital Object Identifier (DOI)

https://doi.org/10.3233/SHTI220485

Publication Info

Published in Challenges of Trustable AI and Added-Value on Health, Volume 294, Summer 2022, pages 407-408.

Rights

© 2022 European Federation for Medical Informatics (EFMI) and IOS Press.

This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).

APA Citation

Panahiazar, M., Chern, Y., Riojas, R., Latif, O. S., Lokala, U., Hadley, D., Sheth, A., & Beygui, R. E. (2022). An Ontology for Cardiothoracic Surgical Education and Clinical Data Analytics. Challenges of Trustable AI and Added-Value on Health, 407–408. https://doi.org/10.3233/SHTI220485