Automatic symptoms identification from a massive volume of unstructured medical consultations using deep neural and BERT models

Automatic symptoms identification from a massive volume of unstructured medical consultations using deep neural and BERT models

Automatic symptom identification plays a crucial role in assisting doctors during the diagnosis process in Telemedicine. In general, physicians spend considerable time on clinical documentation and symptom identification, which is unfeasible due to their full schedule. With text-based consultation s...

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Bibliographic Details
Main Authors: Hossam Faris, Mohammad Faris, Maria Habib, Alaa Alomari
Format: Article
Language:English
Published: Elsevier 2022-06-01
Series:Heliyon
Subjects:
Deep learning
Multi-classification
Multi-label
Telemedicine
Machine learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844022009719

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http://www.sciencedirect.com/science/article/pii/S2405844022009719

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