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Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data
Objective: The high incidence of respiratory diseases has dramatically increased the medical burden under the COVID-19 pandemic in the year 2020. It is of considerable significance to utilize a new generation of information technology to improve the artificial intelligence level of respiratory disea...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861621/ https://www.ncbi.nlm.nih.gov/pubmed/35221528 http://dx.doi.org/10.1007/s10489-022-03222-y |
Sumario: | Objective: The high incidence of respiratory diseases has dramatically increased the medical burden under the COVID-19 pandemic in the year 2020. It is of considerable significance to utilize a new generation of information technology to improve the artificial intelligence level of respiratory disease diagnosis. Methods: Based on the semi-structured data of Chinese Electronic Medical Records (CEMRs) from the China Hospital Pharmacovigilance System, this paper proposed a bi-level artificial intelligence model for the risk classification of acute respiratory diseases. It includes two levels. The first level is a dedicated design of the “BiLSTM+Dilated Convolution+3D Attention+CRF” deep learning model that is used for Chinese Clinical Named Entity Recognition (CCNER) to extract valuable information from the unstructured data in the CEMRs. Incorporating the transfer learning and semi-supervised learning technique into the proposed deep learning model achieves higher accuracy and efficiency in the CCNER task than the popular “Bert+BiLSTM+CRF” approach. Combining the extracted entity data with other structured data in the CEMRs, the second level is a customized XGBoost to realize the risk classification of acute respiratory diseases. Results: The empirical study shows that the proposed model could provide practical technical support for improving diagnostic accuracy. Conclusion: Our study provides a proof-of-concept for implementing a hybrid artificial intelligence-based system as a tool to aid clinicians in tackling CEMR data and enhancing the diagnostic evaluation under diagnostic uncertainty. |
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