  Background and Objectives: Clinical Practice Guidelines (CPGs) represent the
foremost methodology for sharing state-of-the-art research findings in the
healthcare domain with medical practitioners to limit practice variations,
reduce clinical cost, improve the quality of care, and provide evidence based
treatment. However, extracting relevant knowledge from the plethora of CPGs is
not feasible for already burdened healthcare professionals, leading to large
gaps between clinical findings and real practices. It is therefore imperative
that state-of-the-art Computing research, especially machine learning is used
to provide artificial intelligence based solution for extracting the knowledge
from CPGs and reducing the gap between healthcare research/guidelines and
practice. Methods: This research presents a novel methodology for knowledge
extraction from CPGs to reduce the gap and turn the latest research findings
into clinical practice. First, our system classifies the CPG sentences into
four classes such as condition-action, condition-consequences, action, and
not-applicable based on the information presented in a sentence. We use deep
learning with state-of-the-art word embedding, improved word vectors technique
in classification process. Second, it identifies qualifier terms in the
classified sentences, which assist in recognizing the condition and action
phrases in a sentence. Finally, the condition and action phrase are processed
and transformed into plain rule If Condition(s) Then Action format. Results: We
evaluate the methodology on three different domains guidelines including
Hypertension, Rhinosinusitis, and Asthma. The deep learning model classifies
the CPG sentences with an accuracy of 95%. While rule extraction was validated
by user-centric approach, which achieved a Jaccard coefficient of 0.6, 0.7, and
0.4 with three human experts extracted rules, respectively.
