The information theoretic perspective on medical diagnostic inference.
The goal of this work is to present information theory, specifically Claude Shannon's mathematical theory of communication, in a clinical context and elucidate its potential contributions to understanding the process of diagnostic inference. We use probability theory, information theory, and clinical examples to develop information theory as a means to examine uncertainty in diagnostic testing situations. We begin our discussion with a brief review of probability theory as it relates to diagnostic testing. An outline of Shannon's theory of communication theory and how it directly translates to the medical diagnostic process serves as the essential justification for this article. Finally, we introduce the mathematical tools of information theory that allow for an understanding of diagnostic uncertainty and test effectiveness in a variety of contexts. We show that information theory provides a quantitative framework for understanding uncertainty that readily extends to medical diagnostic contexts.
Massachusetts General Hospital, Boston, MA.
Nathaniel A
NA
Eiseman
Matt T
MT
Bianchi
M Brandon
MB
Westover
D001499:Bayes Theorem; D003142:Communication; D003933:Diagnosis; D019937:Diagnostic Techniques and Procedures; D005188:False Negative Reactions; D005189:False Positive Reactions; D007257:Information Theory; D011336:Probability; D035501:Uncertainty