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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...

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

Bruce I Reiner¹

¹Department of Radiology, Veterans Affairs Maryland Healthcare System, 10 North Greene Street, Baltimore, MD, 21201, USA. breiner1@comcast.net.

Journal of Digital Imaging

|December 24, 2017

Summary

Standardizing how medical reports express uncertainty can improve communication and patient care. Computerized methods can analyze this uncertainty language for better diagnostic confidence and decision support.

Keywords:

Data mining Machine learning Natural language processing Report uncertainty

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Area of Science:

Medical Informatics
Natural Language Processing
Clinical Decision Support

Background:

Uncertainty in medical reports causes miscommunication and clinical issues.
Standardized methods are needed to characterize and quantify uncertainty language.

Purpose of the Study:

To develop a standardized methodology for characterizing and quantifying uncertainty in medical reports.
To provide context on diagnostic confidence and accuracy for report authors and readers.

Main Methods:

Utilizing computerized strategies such as string search.
Employing natural language processing and understanding (NLP/NLU).
Applying histogram analysis, topic modeling, and machine learning.

Main Results:

Derived uncertainty data allows objective, real-time analysis of report uncertainty.
Potential to correlate uncertainty data with outcomes for decision support.

Conclusions:

A standardized approach to analyzing uncertainty language in medical reports is feasible.
This analysis can enhance point-of-care decision support and clinical impact.