Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data
Applied Intelligence
; : 1-18, 2022.
Article
Dans Anglais
| EuropePMC | ID: covidwho-1695769
ABSTRACT
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.
Collection:
Bases de données des oragnisations internationales
Base de données:
EuropePMC
Type d'étude:
Étude pronostique
langue:
Anglais
Revue:
Applied Intelligence
Année:
2022
Type de document:
Article
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