Using Soft Computer Techniques on Smart Devices for Monitoring Chronic Diseases: the CHRONIOUS case

TL;DR

This paper presents the CHRONIOUS platform, which integrates machine learning, rule-based decision support, and sensor data on smart devices to monitor chronic diseases like COPD and CKD, aiding clinicians in decision-making.

Contribution

It demonstrates how machine learning and rule-based systems can be implemented on smart devices for real-time chronic disease monitoring and alerting.

Findings

Machine learning algorithms can effectively detect health deterioration trends.

The platform supports remote monitoring of COPD and CKD patients.

Integration of modules enhances decision support for clinicians.

Abstract

CHRONIOUS is an Open, Ubiquitous and Adaptive Chronic Disease Management Platform for Chronic Obstructive Pulmonary Disease(COPD) Chronic Kidney Disease (CKD) and Renal Insufficiency. It consists of several modules: an ontology based literature search engine, a rule based decision support system, remote sensors interacting with lifestyle interfaces (PDA, monitor touchscreen) and a machine learning module. All these modules interact each other to allow the monitoring of two types of chronic diseases and to help clinician in taking decision for cure purpose. This paper illustrates how some machine learning algorithms and a rule based decision support system can be used in smart devices, to monitor chronic patient. We will analyse how a set of machine learning algorithms can be used in smart devices to alert the clinician in case of a patient health condition worsening trend.