Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data
Applied Intelligence
; : 1-18, 2022.
Article
in English
| 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.
Search on Google
Collection:
Databases of international organizations
Database:
EuropePMC
Type of study:
Prognostic study
Language:
English
Journal:
Applied Intelligence
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS