Dataset Information

A Review of the Application of Information Theory to Clinical Diagnostic Testing.

ABSTRACT: The fundamental information theory functions of entropy, relative entropy, and mutual information are directly applicable to clinical diagnostic testing. This is a consequence of the fact that an individual's disease state and diagnostic test result are random variables. In this paper, we review the application of information theory to the quantification of diagnostic uncertainty, diagnostic information, and diagnostic test performance. An advantage of information theory functions over more established test performance measures is that they can be used when multiple disease states are under consideration as well as when the diagnostic test can yield multiple or continuous results. Since more than one diagnostic test is often required to help determine a patient's disease state, we also discuss the application of the theory to situations in which more than one diagnostic test is used. The total diagnostic information provided by two or more tests can be partitioned into meaningful components.

SUBMITTER: Benish WA

PROVIDER: S-EPMC7516534 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Review of the Application of Information Theory to Clinical Diagnostic Testing.

Benish William A WA

Entropy (Basel, Switzerland) 20200114 1

The fundamental information theory functions of entropy, relative entropy, and mutual information are directly applicable to clinical diagnostic testing. This is a consequence of the fact that an individual's disease state and diagnostic test result are random variables. In this paper, we review the application of information theory to the quantification of diagnostic uncertainty, diagnostic information, and diagnostic test performance. An advantage of information theory functions over more esta ...[more]

PMID: 33285872

Similar Datasets

Project description:BackgroundGeneral Practitioners (GPs) employ strategies to identify and retrieve medical evidence for clinical decision making which take workload and time constraints into account. Optimal Foraging Theory (OFT) initially developed to study animal foraging for food is used to explore the information searching behaviour of General Practitioners. This study is the first to apply foraging theory within this context.Study objectives were: 1. To identify the sequence and steps deployed in identifiying and retrieving evidence for clinical decision making. 2. To utilise Optimal Foraging Theory to assess the effectiveness and efficiency of General Practitioner information searching.MethodsGPs from the Wellington region of New Zealand were asked to document in a pre-formatted logbook the steps and outcomes of an information search linked to their clinical decision making, and fill in a questionnaire about their personal, practice and information-searching backgrounds.ResultsA total of 115/155 eligible GPs returned a background questionnaire, and 71 completed their information search logbook. GPs spent an average of 17.7 minutes addressing their search for clinical information. Their preferred information sources were discussions with colleagues (38% of sources) and books (22%). These were the two most profitable information foraging sources (15.9 min and 9.5 min search time per answer, compared to 34.3 minutes in databases). GPs nearly always accessed another source when unsuccessful (95% after 1st source), and frequently when successful (43% after 2nd source). Use of multiple sources accounted for 41% of searches, and increased search success from 70% to 89%.ConclusionsBy consulting in foraging terms the most 'profitable' sources of information (colleagues, books), rapidly switching sources when unsuccessful, and frequently double checking, GPs achieve an efficient trade-off between maximizing search success and information reliability, and minimizing searching time. As predicted by foraging theory, GPs trade time-consuming evidence-based (electronic) information sources for sources with a higher information reward per unit time searched. Evidence-based practice must accommodate these 'real world' foraging pressures, and Internet resources should evolve to deliver information as effectively as traditional methods of information gathering.

Dataset Information

A Review of the Application of Information Theory to Clinical Diagnostic Testing.

Publications

A Review of the Application of Information Theory to Clinical Diagnostic Testing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets