Publicação
Development of an innovative and transparent symptom checker with a focus on multi-label classification
| Resumo: | 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. |
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| Autores principais: | Petry, Hannah |
| Assunto: | Deep learning Machine learning Classification Speech to text Natural language processing Medical symptom checker Streamlit |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | 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. |
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