Certainty Modeling of a Decision Support System for Mobile Monitoring of Exercise induced Respiratory Conditions

TL;DR

This paper presents a novel certainty theory-based decision support system for mobile health monitoring of exercise-induced respiratory conditions, enhancing reliability and aiding clinical decision-making.

Contribution

It introduces a certainty modeling approach for mobile DSS to improve inexact reasoning and reliability in respiratory health monitoring.

Findings

Effective detection of respiratory distress triggers

Enhanced decision support accuracy in mobile health systems

Facilitates patient self-management and clinical assessment

Abstract

Mobile health systems in recent times, have notably improved the healthcare sector by empowering patients to actively participate in their health, and by facilitating access to healthcare professionals. Effective operation of these mobile systems nonetheless, requires high level of intelligence and expertise implemented in the form of decision support systems (DSS). However, common challenges in the implementation include generalization and reliability, due to the dynamics and incompleteness of information presented to the inference models. In this paper, we advance the use of ad hoc mobile decision support system to monitor and detect triggers and early symptoms of respiratory distress provoked by strenuous physical exertion. The focus is on the application of certainty theory to model inexact reasoning by the mobile monitoring system. The aim is to develop a mobile tool to assist patients in managing their conditions, and to provide objective clinical data to aid physicians in the screening, diagnosis, and treatment of the respiratory ailments. We present the proposed model architecture and then describe an application scenario in a clinical setting. We also show implementation of an aspect of the system that enables patients in the self-management of their conditions.