Discovering hidden knowledge through auditing clinical diagnostic knowledge bases.

Journal: Journal of biomedical informatics

Published Date: Jun 22, 2018

Abstract

OBJECTIVE: Evaluate potential for data mining auditing techniques to identify hidden concepts in diagnostic knowledge bases (KB). Improving completeness enhances KB applications such as differential diagnosis and patient case simulation.

Authors

Matthew C Lenert

Dept. of Biomedical Informatics, Vanderbilt University, 2525 West End Ave. Suite 1475, Nashville, TN 37203, USA. Electronic address: matthew.c.lenert@vanderbilt.edu.
Colin G Walsh

Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, United States.
Randolph A Miller

Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee.

Keywords

Algorithms Bayes Theorem Data Mining Diagnosis, Computer-Assisted Diagnosis, Differential Expert Systems Humans Knowledge Bases Machine Learning Medical Informatics Models, Statistical ROC Curve

External Resources

View on PubMed Access via DOI PubMed (29940263)

Discovering hidden knowledge through auditing clinical diagnostic knowledge bases.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals