A probabilistic ontology-based platform for self-learning context-aware healthcare applications - Ongenae, Femke; Claeys, Maxim; Dupont, Thomas; Kerckhove, Wannes; Verhoeve, Piet; Dhaene, Tom; De Turck, Filip (Expert systems with applications, 2013)
•The architecture of a self-learning, ontology-based framework is presented.•Framework enables context-aware applications to change their behavior at run-time.•Knowledge added to the ontology is given a probability to convey its reliability.•An implemented use case is presented: mining the reasons for patients’ nurse calls.•Datasets of size >1000 with <5% noise yield correct results in negligible time. Context-aware platforms consist of dynamic algorithms that take the context information into account to adapt the behavior of the applications. The relevant context information is modeled in a context model. Recently, a trend has emerged towards capturing the context in an ontology, which formally models the concepts within a certain domain, their relations and properties. Although much research has been done on the subject, the adoption of context-aware services in healthcare is lagging behind what could be expected. The main complaint made by users is that they had to significantly alter workflow patterns to accommodate the system. When new technology is introduced, the behavior of the users changes to adapt to it. Moreover, small differences in user requirements often occur between different environments where the application is deployed. However, it is difficult to foresee these changes in workflow patterns and requirements at development time. Consequently, the context-aware applications are not tuned towards the needs of the users and they are required to change their behavior to accommodate the technology instead of the other way around. To tackle this issue, a self-learning, probabilistic, ontology-based framework is proposed, which allows context-aware applications to adapt their behavior at run-time. It exploits the context information gathered in the ontology to mine for trends and patterns in the behavior of the users. These trends are then prioritized and filtered by associating probabilities, which express their reliability. This new knowledge and their associated probabilities are then integrated into the context model and dynamic algorithms. Finally, the probabilities are in- or decreased, according to context and behavioral information gathered about the usage of the learned information. A use case is presented to illustrate the applicability of the framework, namely mining the reasons for patients’ nurse call light use to automatically launch calls. Detecting Systemic Inflammatory Response Syndrome (SIRS) as a reason for nurse calls is used as a realistic scenario to evaluate the correctness and performance of the proposed framework. It is shown that correct results are achieved when the dataset contains at least 1000 instances and the amount of noise is lower than 5%. The execution time and memory usage are also negligible for a realistic dataset, i.e., below 100ms and 10MB.