Journal of Digital Imaging

, Volume 31, Issue 2, pp 145–149 | Cite as

Quantitative Analysis of Uncertainty in Medical Reporting: Creating a Standardized and Objective Methodology

Article

Abstract

Uncertainty in text-based medical reports has long been recognized as problematic, frequently resulting in misunderstanding and miscommunication. One strategy for addressing the negative clinical ramifications of report uncertainty would be the creation of a standardized methodology for characterizing and quantifying uncertainty language, which could provide both the report author and reader with context related to the perceived level of diagnostic confidence and accuracy. A number of computerized strategies could be employed in the creation of this analysis including string search, natural language processing and understanding, histogram analysis, topic modeling, and machine learning. The derived uncertainty data offers the potential to objectively analyze report uncertainty in real time and correlate with outcomes analysis for the purpose of context and user-specific decision support at the point of care, where intervention would have the greatest clinical impact.

Keywords

Report uncertainty Data mining Natural language processing Machine learning 

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Copyright information

© Society for Imaging Informatics in Medicine 2017

Authors and Affiliations

  1. 1.Department of RadiologyVeterans Affairs Maryland Healthcare SystemBaltimoreUSA

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