{'fraction_non_alphanumeric': 0.02633889376646181, 'fraction_numerical': 0.0052677787532923615, 'mean_word_length': 5.666666666666667, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
SEPHYRES 2: applying semantic-pseudo-fuzzy methods in medical diagnostic ontologies

To date, ontology-based medical diagnostic systems have not incorporated complete descriptions of diseases and their semantic relations with signs and symptoms. In SEPHYRES 1, a pain-focused-only solution was proposed which applied not only general semantic reasoners, but also weight spreading techniques. Proceeding the research, we developed the SEPHYRES knowledge base to address all signs, symptoms and complex relations, including similar terms, terms with the variant generality level, composed terms, terms which include several other terms based on medical diagnostic criteria. The evaluation outcomes, in terms used in patient's descriptive history in both of the MEDSCAPE and PubMed case studies, showed that the recall amount of system-oriented evaluation was about 90% provided that just top ten results were considered. Furthermore, the Wilcoxon signed-ranked test between SEPHYRES 2 and the best symptom checker, Isabel engine power, showed that the SEPHYRES 2 significantly improved the matching process of the patient's disease profiles.