alexa Non-Additive Measures: A Theoretical Approach to Medical Decision-Making
ISSN: 2165- 7866

Journal of Information Technology & Software Engineering
Open Access

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Research Article

Non-Additive Measures: A Theoretical Approach to Medical Decision-Making

François Modave* and Navkiran K Shokar

Department of Computer Science, Jackson State University, John R. Lynch Street, Jackson, USA

*Corresponding Author:
François Modave
Associate Professor and Chair, Department of Computer Science
Jackson State University, John R. Lynch Street, Jackson, USA
E-mail: [email protected]

Received Date: June 12, 2013; Accepted Date: December 05, 2013; Published Date: December 11, 2013

Citation: Modave F, Shokar NK (2013) Non-Additive Measures: A Theoretical Approach to Medical Decision-Making. J Inform Tech Softw Eng 3:124. doi:10.4172/2165-7866.1000124

Copyright: © 2013 Modave F, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Informatics-based decision-making aids are becoming an essential component of clinical care from both a physician and patient perspective. Although additive approaches are used with a certain degree of success in a medical context, they often suffer from an inability to conveniently represent dependencies, which is certainly desirable in practice. To address this drawback, we present the concepts of non-additive measures, and non-additive integration, as well as Shapley values and interaction indices to a clinical framework, and show how they can be used to develop robust and reliable computing tools that support informed and shared decision-making. We also present an extension of these tools that allow us to manage the inherent uncertainty and imprecision of data, and help us address value clarification. To set ideas, we focus on presenting algorithms to improve shared decision-making for colorectal cancer screening, however, the framework presented here is general, and can be applied to a wide variety of clinical decision problems.


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