Natural Language Processing method to Unravel Long COVID's clinical condition in hospitalized patients

Abstract Long COVID is characterized by persistent symptoms beyond established timeframes, presenting a significant challenge in understanding its clinical manifestations and implications. In this study, we present a novel application of natural language processing (NLP) techniques to automatically extract unstructured data from a Long COVID survey conducted at a prominent university hospital in São Paulo, Brazil. Our phonetic text clustering (PTC) method enables the exploration of unstructured EHR data to unify different written forms of similar terms into a single phonemic representation. We use n-gram text analysis to detect compound words and negated terms in Portuguese-BR, focusing on medical conditions and symptoms related to Long COVID. By leveraging NLP, we aim to contribute to a deeper understanding of this chronic condition and its implications for healthcare systems worldwide. The model developed in this study has the potential for scalability and applicability in other healthcare settings, facilitating broader research efforts and informing clinical decision-making for Long COVID patients.