Motivating Scenario: The metabolic syndrome (MS) is a cluster of health conditions that occur together and increase the risk of heart disease, stroke and diabetes. As the availability of wearable sensors is becoming more popular, the collection of frequent physiological data from individuals has become easier than ever. This raises a need for new models that interpret continuous physiological values and provide meaningful interpretation for patients and caregivers. One way of interpreting these data is by automating existing evidence based guidelines. The assumption is that, by combining different clinical guidelines relating to the metabolic syndrome with the physiological data of the patient, we can predict deterioration states that may require medical attention. Such solution can assist caregivers in identifying high-risk patients and provide patient tailored interventions.