Modeling diseases with Stream X Machine

Jayatilake, Seenrath; Ogunshile, Emmanuel; Aydin, Mehmet; Phung, Khoa

Abstract

At present the world is moving towards alternative medicine and behavioural alteration for treating, managing, and preventing chronical diseases. With the individuality of the human beings has added more complexity in a domain where very high accuracy is demanded. Formal methods have been proven to be occupied in critical system development. This paper introduces a generic disease model called Stream X-Machine Disease Model (SXMDM) based on X-Machine theory. SXMDM has been developed as a proof of concept that formal methods, especially Stream X-Machines, can be employed to model medical conditions or diseases. We have conducted an experiment on modelling an actual disease using a case study of type 2 diabetes. The results of the experiment illustrates that the proposed SXMDM is capable of modelling chronic diseases.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: CONISOFT 2021 : IEEE 9th International Conference on Software Engineering Research and Innovation
• Start Date: Oct 25, 2021
• End Date: Oct 29, 2021
• Acceptance Date: Jul 23, 2021
• Online Publication Date: Dec 28, 2021
• Publication Date: Dec 28, 2021
• Deposit Date: Aug 6, 2021
• Publicly Available Date: Jan 14, 2022
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Pages: 61-68
• Book Title: 2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT)
• ISBN: 9781665443623
• DOI: https://doi.org/10.1109/CONISOFT52520.2021.00020
• Keywords: component; X-Machine; Stream X-Machine; Formal Methods; Chromical Diseases; Type II Diabetes
• Public URL: https://uwe-repository.worktribe.com/output/7605710
• Publisher URL: http://conisoft.org/2021/