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Medical guidelines have a significant role in the field of evidence-based medical treatment. The content of a medical guideline is based on a systematic review of clinical evidence with instructions and recommendations that clinicians can refer to. Most of the guidelines are available in an unstructured text format. Hence, clinicians must take a considerable time to search and find relevant recommendations in their semantic context. Using Machine Learning algorithms, automatic information extraction from medical guidelines has recently become possible. We present a novel system for information extraction and a fuzzy rule database developed for clinical guidelines. The proposed system, dubbed NLP-FUZZY, combines capabilities of Natural Language Processing (NLP) and Fuzzy Logic approaches. First, the NLP-FUZZY performs a semantic extraction of medical guidelines using a bi-directional Long short-term …