In order to maintain and improve the quality of care without exploding costs, healthcare systems are undergoing a paradigm shift from patient care in the hospital to patient care at home. Remote patient management (RPM) systems offer a great potential in reducing hospitalization costs and worsening of symptoms for patients with chronic diseases, e.g., heart failure and diabetes. Different types of data collected by RPM systems provide an opportunity for personalizing information services, and alerting medical personnel about the changing conditions of the patient. In this work we focus on a particular problem of patient modeling that is the hospitalization prediction. We consider the problem definition, our approach to this problem, highlight the results of the experimental study and reflect on their use in decision making.
|Title of host publication||Adjunct Proceedings of the 18th International Conference on User Modeling, Adaptation, and Personalization: Posters and Demonstrations (UMAP 2010, Big Island HI, USA, June 2-24, 2010)|
|Editors||F. Bohnert, L.M. Quiroga|
|Publication status||Published - 2010|