A Context Awareness System for Clinical Environments

This study addresses the complex management of patient-related information in hospitals and clinical settings. This information includes treatments, medications, vital signs, patient locations, and data exchange between healthcare professionals. The lack of effective synchronization between these elements often delays timely care. This study proposes an architecture based on a semantic representation model that articulates the various components of a hospital environment. This model supports decision-making in healthcare by facilitating inferences from the environment. The semantic model serves as a basis for executing predefined rules that trigger actions through a reasoner, resulting in notifications, such as administering medications or responding to abnormal vital signs. The model integrates supervised learning to improve the accuracy of alerts. The experiment focused on monitoring vital sign parameters, such as Spo2, body temperature, and heart rate. The combination of semantic representation modeling and machine learning algorithms demonstrates a robust approach for improving the efficiency and accuracy of healthcare alerts in clinical settings.