Thinking eHealth: A Mathematical Background of an Individual Health Status Monitoring System to Empower Young People to Manage Their Health

Izabella V. Lokshina and Michael R. Bartolacci
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Izabella V. Lokshina: MIS, SUNY, Oneonta, NY, USA
Michael R. Bartolacci: IST, Penn State University Berks, Reading, PA, USA

International Journal of Interdisciplinary Telecommunications and Networking (IJITN), 2014, vol. 6, issue 3, 27-36

Abstract: This paper focuses on a mathematical background of an individual health status monitoring system to empower young people to manage their health. The proposed health status monitoring system uses symptoms observed with mobile sensing devices and prior information about health and environment (provided it exists) to define individual physical and psychological status. It assumes that a health status identification process is influenced by many parameters and conditions. It has a flexible logical inference system providing positive psychological influence on young people since full acceptance of recommendations on their behavioral changes towards healthy lifestyles is reached and a correct interpretation is guaranteed. The model and algorithms of the individual health status monitoring system are developed based on the composition inference rule in Zadeh's fuzzy logic. The model allows us to include in the algorithms of logical inference the possibility of masking (by means of a certain health condition) the symptoms of other health situations as well as prior information (if it exists) regarding health and environment. The algorithms are generated by optimizing the truth of a single natural “axiom”, which connects an individual health status (represented by classes of health situations) with symptoms and matrices of influence of health situations on symptoms and masking of symptoms. The new algorithms are fairly different from traditional algorithms, in which the result is produced in the course of numerous single processing rules. Therefore, the use of a composition inference rule makes a health status identification process faster and the obtained results more precise and efficient comparing to traditional algorithms.

Date: 2014
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