Leng Jiewu, Wang Dewen, Ma Xin, Yu Pengjiu, Wei Li, Chen Wenge
State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou, 510006 Guangdong China.
Department of Information Systems, Chengdu Research Institute, City University of Hong Kong, Hong Kong, China.
Appl Intell (Dordr). 2022;52(11):13114-13131. doi: 10.1007/s10489-022-03222-y. Epub 2022 Feb 22.
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.
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.
The empirical study shows that the proposed model could provide practical technical support for improving diagnostic accuracy.
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.
2020年新冠疫情期间，呼吸系统疾病的高发病率大幅增加了医疗负担。利用新一代信息技术提高呼吸系统疾病诊断的人工智能水平具有重要意义。
基于中国医院药物警戒系统的中文电子病历（CEMR）半结构化数据，本文提出了一种用于急性呼吸系统疾病风险分类的双层人工智能模型。它包括两个层次。第一层是专门设计的“双向长短期记忆网络+扩张卷积+三维注意力+条件随机场”深度学习模型，用于中文临床命名实体识别（CCNER），从CEMR中的非结构化数据中提取有价值的信息。在所提出的深度学习模型中融入迁移学习和半监督学习技术，在CCNER任务中比流行的“伯特+双向长短期记忆网络+条件随机场”方法具有更高的准确性和效率。将提取的实体数据与CEMR中的其他结构化数据相结合，第二层是定制的极端梯度提升模型，以实现急性呼吸系统疾病的风险分类。
实证研究表明，所提出的模型可为提高诊断准确性提供实际技术支持。
我们的研究为实施基于混合人工智能的系统提供了概念验证，作为一种工具来帮助临床医生处理CEMR数据，并在诊断不确定性下加强诊断评估。
