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Development of an innovative and transparent symptom checker with a focus on multi-label classification
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Resumo(s)
This paper presents the creation of a medical symptom checker with state-of-the-art machine
and deep learning technologies. It examines the use and development of speech-to-text, natural
language processing, and classification models, which are trained on medical datasets. All
models are discussed, highlighting their advantages and disadvantages for the study. Moreover,
the paper introduces a web application which provides a user-friendly interface, allowing users
to interact with the models and showcase the results. Finally, the paper offers an outlook on the
future use cases of the application and how it may improve healthcare outcomes.
Descrição
Palavras-chave
Deep learning Machine learning Classification Speech to text Natural language processing Medical symptom checker Streamlit