Krause Paul
Department of Computer Science, University of Surrey, United Kingdom.
Stud Health Technol Inform. 2019 Jul 30;263:23-34. doi: 10.3233/SHTI190108.
Information theory has gained application in a wide range of disciplines, including statistical inference, natural language processing, cryptography and molecular biology. However, its usage is less pronounced in medical science. In this chapter, we illustrate a number of approaches that have been taken to applying concepts from information theory to enhance medical decision making. We start with an introduction to information theory itself, and the foundational concepts of information content and entropy. We then illustrate how relative entropy can be used to identify the most informative test at a particular stage in a diagnosis. In the case of a binary outcome from a test, Shannon entropy can be used to identify the range of values of test results over which that test provides useful information about the patient's state. This, of course, is not the only method that is available, but it can provide an easily interpretable visualization. The chapter then moves on to introduce the more advanced concepts of conditional entropy and mutual information and shows how these can be used to prioritise and identify redundancies in clinical tests. Finally, we discuss the experience gained so far and conclude that there is value in providing an informed foundation for the broad application of information theory to medical decision making.
信息论已在广泛的学科中得到应用，包括统计推断、自然语言处理、密码学和分子生物学。然而，它在医学领域的应用并不那么显著。在本章中，我们阐述了一些为应用信息论概念以加强医学决策而采取的方法。我们首先介绍信息论本身，以及信息内容和熵的基础概念。然后我们说明如何使用相对熵来确定诊断特定阶段最具信息量的检验。对于检验的二元结果，香农熵可用于确定检验结果的取值范围，在此范围内该检验可提供有关患者状态的有用信息。当然，这不是唯一可用的方法，但它能提供易于解释的可视化。本章接着介绍条件熵和互信息等更高级的概念，并展示如何利用这些概念对临床试验进行优先级排序和识别冗余。最后，我们讨论了目前所获得的经验，并得出结论，为信息论在医学决策中的广泛应用提供明智的基础是有价值的。
