Enhanced neurologic concept recognition using a named entity recognition model based on transformers

• Principais autores: Sima Azizi (Autor), Daniel B. Hier (Autor), Donald C. Wunsch II (Autor)
• Formato: Livro
• Publicado em: Frontiers Media S.A., 2022-12-01T00:00:00Z.
• Assuntos: article
• Acesso em linha: Connect to this object online.
• Notes: 2673-253X; 10.3389/fdgth.2022.1065581
• Keywords: named entity recognition; clinical concepts; concept extraction; phenotype; transformers; natural language processing; Medicine; R; Public aspects of medicine; RA1-1270; Electronic computers. Computer science; QA75.5-76.95
• Source: Frontiers in Digital Health, Vol 4 (2022)

Although deep learning has been applied to the recognition of diseases and drugs in electronic health records and the biomedical literature, relatively little study has been devoted to the utility of deep learning for the recognition of signs and symptoms. The recognition of signs and symptoms is critical to the success of deep phenotyping and precision medicine. We have developed a named entity recognition model that uses deep learning to identify text spans containing neurological signs and symptoms and then maps these text spans to the clinical concepts of a neuro-ontology. We compared a model based on convolutional neural networks to one based on bidirectional encoder representation from transformers. Models were evaluated for accuracy of text span identification on three text corpora: physician notes from an electronic health record, case histories from neurologic textbooks, and clinical synopses from an online database of genetic diseases. Both models performed best on the professionally-written clinical synopses and worst on the physician-written clinical notes. Both models performed better when signs and symptoms were represented as shorter text spans. Consistent with prior studies that examined the recognition of diseases and drugs, the model based on bidirectional encoder representations from transformers outperformed the model based on convolutional neural networks for recognizing signs and symptoms. Recall for signs and symptoms ranged from 59.5% to 82.0% and precision ranged from 61.7% to 80.4%. With further advances in NLP, fully automated recognition of signs and symptoms in electronic health records and the medical literature should be feasible.