Towards an Integrated Approach to Monitor and Analyse Health Care Data using Relational Databases

In modern patient monitoring systems a tremendous amount of data is gathered, stored, and analysed to support doctors in making important decisions in a timely manner. To this end, different types of data from different sources have to be processed such as sensor readings of patients vitals, meta-data like the age and weight of a patient, and historical data like performed treatments or therapies. Most of the data is low-level and has an intrinsically temporal nature which need to be preprocessed for doctors to find high-level information in an efficient way. In monitoring scenarios however, aside from the detection of critical situations of patients, medics are often interested in the phases in which their patients are most probably in. In this paper, we show how phase analysis can considerably reduce the syntactic complexity of continuous queries as provided by the Continuous Query Language (CQL). Such phases provide an advanced and higher level of abstraction enabling effective and intuitive formulation of queries comparing to classic CQL. This can greatly improve the development efficiency and reduce the maintenance complexity of patient monitoring system.