Probabilistic scoring of validated insights for personal health services

In connected health services automatic discovery of recurring patterns and correlations, or insights, provides many interesting opportunities for the personalization of the services. In this paper the focus is on insight mining for a health coaching service. The basic idea in the proposed method is to generate a large number of insight candidates which have been pre-validated with domain experts and to score them using the data. The dynamic performance of the scoring is studied with a collection of lifestyle sensor data from volunteers. The proposed method is compared to a conventional data mining approach based on the Apriori algorithm. We demonstrate that the proposed method gives significantly more variability among the subjects and types of insights it finds which may reflect better the underlying statistics of individual lifestyle patterns of the different subjects.