Cargando…
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: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
|
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 |
_version_ | 1784654925089734656 |
---|---|
author | Leng, Jiewu Wang, Dewen Ma, Xin Yu, Pengjiu Wei, Li Chen, Wenge |
author_facet | Leng, Jiewu Wang, Dewen Ma, Xin Yu, Pengjiu Wei, Li Chen, Wenge |
author_sort | Leng, Jiewu |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8861621 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-88616212022-02-22 Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data Leng, Jiewu Wang, Dewen Ma, Xin Yu, Pengjiu Wei, Li Chen, Wenge Appl Intell (Dordr) Article 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. Springer US 2022-02-22 2022 /pmc/articles/PMC8861621/ /pubmed/35221528 http://dx.doi.org/10.1007/s10489-022-03222-y Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Leng, Jiewu Wang, Dewen Ma, Xin Yu, Pengjiu Wei, Li Chen, Wenge Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data |
title | Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data |
title_full | Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data |
title_fullStr | Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data |
title_full_unstemmed | Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data |
title_short | Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data |
title_sort | bi-level artificial intelligence model for risk classification of acute respiratory diseases based on chinese clinical data |
topic | Article |
url | 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 |
work_keys_str_mv | AT lengjiewu bilevelartificialintelligencemodelforriskclassificationofacuterespiratorydiseasesbasedonchineseclinicaldata AT wangdewen bilevelartificialintelligencemodelforriskclassificationofacuterespiratorydiseasesbasedonchineseclinicaldata AT maxin bilevelartificialintelligencemodelforriskclassificationofacuterespiratorydiseasesbasedonchineseclinicaldata AT yupengjiu bilevelartificialintelligencemodelforriskclassificationofacuterespiratorydiseasesbasedonchineseclinicaldata AT weili bilevelartificialintelligencemodelforriskclassificationofacuterespiratorydiseasesbasedonchineseclinicaldata AT chenwenge bilevelartificialintelligencemodelforriskclassificationofacuterespiratorydiseasesbasedonchineseclinicaldata |