ArticleApplied intelligence (Dordrecht, Netherlands)2022
Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data.
Article in Applied intelligence (Dordrecht, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 12 citations in OpenAlex.
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Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe 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.
methodsBased 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.
resultsThe empirical study shows that the proposed model could provide practical technical support for improving diagnostic accuracy.
conclusionOur 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.