A Novel NLP-FUZZY System Prototype for Information Extraction from Medical Guidelines

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 memory (LSTM). Subsequently, using the extracted semantic, it creates fuzzy rules, which are able to recognize new cases in a learning domain while predicting and extract the grade of recommendation. In order to test the NLP-FUZZY system, we compared its performance with state-of-the-art NLP approaches for clinical information extraction.

Lejla Begic Fazlic , Ahmed Hallawa , Anke Schmeink , Arne Peine , Lukas Martin and Guido Dartmann

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