Predicting feedback compliance in a teletreatment application

Health care provision is facing resourcing challenges which will further increase in the 21^st century. Health care mediated by technology is widely seen as one important element in the struggle to maintain existing standards of care. Personal health monitoring and treatment systems with a high degree of autonomic operation will be required to support self-care. Such systems must provide many services and in most cases must incorporate feedback to patients to advise them how to manage the daily details of their treatment and lifestyle changes. As in many other areas of healthcare, patient compliance is however an issue. In this experiment we apply machine learning techniques to three corpora containing data from trials of body worn systems for activity monitoring and feedback. The overall objective is to investigate how to improve feedback compliance in patients using personal monitoring and treatment systems, by taking into account various contextual features associated with the feedback instances. In this article we describe our first machine learning experiments. The goal of the experiments is twofold: to determine a suitable classification algorithm and to find an optimal set of contextual features to improve the performance of the classifier. The optimal feature set was constructed using genetic algorithms. We report initial results which demonstrate the viability of this approach.