Modelagem Fuzzy Intuicionista no Reconhecimento de Padrões em Registros no Prontuário Eletrônico de Pacientes atendidos no Hospital Universitário durante a Pandemia da Covid-19

Abstract

Text Mining corresponds to pre-processing for the use of algorithms, as it organizes, links and transforms the contents of written language into reliable information that anticipates the subsequent use of modeling that seeks pattern recognition. It is noteworthy that 80% of the data generated is unstructured and of these, 80% are in textual format that deals with uncertainty and ambiguity. Information Retrieval and Natural Language Processing account for the frequency of occurrence of words, which allows the approach of Inferential Statistics and Computational Intelligence. Objective: Applying Fuzzy Intuitionist modeling to obtain patterns according to linguistic terms in records in the electronic medical record of patients treated in a Public University Hospital Unit during the Covid-19 pandemic. Method: Case study, Fuzzy Intuitionist Grouping was chosen, which considers the degree of relevance, non- pertinence and hesitation to describe scenarios. Applying this modeling, which was used to obtain patterns according to linguistic terms, we used the records made in the electronic medical records of patients at the Pedro Ernesto University Hospital during the period of the Covid-19 pandemic. The unstructured information, written incorrectly and not complying with the International Classification of Diseases, went through the Pre-Processing stage, where variables were created for entry into the Fuzzy System. The Elbow method was applied to indicate the probable number of clusters and ratification by the Fuzzy Intuitionist C-Means model. Results: Pre-processing organized and adjusted the data and enabled the construction of a textual database associated with the incidence of Covid-19 in cases treated at HUPE, which can be used in other research. Two Standard Classes were established to allow the classification of a patient suffering from Covid-19 based on the Cosine of the Intuitionist Fuzzy Sets according to the symptoms: fever, shortness of breath and fatigue; and comorbidities: hypertension, cerebrovascular disease and cardiovascular disease. Conclusion: The results confirm that a patient can be classified into a certain standard class by estimating the pertinences of the Fuzzy Intuitionist Logic System, taking into account the relevance of the similarity.