Title : Adaptive Healthcare System
Chronic obstructive pulmonary disease (COPD) is one of the most severe public health problems worldwide. Pervasive computing technology creates a new opportunity to redesign the traditional pattern of medical system. While many pervasive healthcare systems are currently found in the literature, there is little published research on the effectiveness and adaptation of these paradigms in the medical context. We designed and validated a rule-based ontology framework for COPD patients. Unlike conventional systems, this work presents a new vision of telemedicine and remote care solutions that will promote individual self-management and autonomy for COPD patients through an advanced decision-making technique. This framework organizes and manages patients’ data and information, as well as helps doctors and medical experts in diagnosing disease and taking precluding procedure to avoid exacerbation as much as possible. In this talk, I will highlight the main components of this framework and show methods to adapt it based on the context of patients.
Based on the analyzed data, we found that rules accuracy estimates were 89% for monitoring vital signs, and environmental factors. The originality of the proposed approach consists in its methodology to prove the efficiency of this model in simulated examples of real-life scenarios based on collaborative data analysis, recognized by specialized medical experts.
Our findings proved that dynamic thresholds can enhance existing telemonitoring systems and make a valuable contribution to identifying the health status of COPD patients. Three main conclusions can be drawn from this work. Firstly, an ontology-based system can provide a more efficient way to deal with medical data. Secondly, our framework suggests reliable recommendations. Thirdly, the results support the importance of context where it demonstrates that context variables have a strong influence on the accuracy of decisions.