While the PICO framework is widely used by clinicians for clinical question formulation when querying the medical literature, it does not have the expressiveness to explicitly capture medical findings based on any standard. In addition, findings extracted from the literature are represented as free-text, which is not amenable to computation. This research extends the PICO framework with Observation elements, which capture the observed effect that an Intervention has on an Outcome, forming Intervention-Observation-Outcome triplets. In addition, we present a framework to normalize Observation elements with respect to their significance and the direction of the effect, as well as a rule-based approach to perform the normalization of these attributes. Our method achieves macro-averaged F1 scores of 0.82 and 0.73 for identifying the significance and direction attributes, respectively.
Keywords: Evidence-based Medicine; Natural Language Processing; Text Mining.