PICO extraction by combining the robustness of machine-learning methods with the rule-based methods

Machine-learning methods (MLMs) are robust methods in the extraction of the information; they have been also used in the extraction of PICO elements in order to answer clinical questions; MLMs are only used at coarse-grained level in PICO extraction, because of lack of training corpora for PICO at the fine-grained level. Coarse-grained level cannot explore the semantics within the sentence for use as a means of relevance between different answers. We propose a hybrid approach combining the robustness of MLMs and the fine grained level of RBMs to enhance PICO extraction process and facilitate the validity and the pertinence of the answers to clinic questions formulated with the PICO framework.