Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods

Autor: Li Li, Alimu Ayiguli, Qiyun Luan, Boyi Yang, Yilamujiang Subinuer, Hui Gong, Abudureherman Zulipikaer, Jingran Xu, Xuemei Zhong, Jiangtao Ren, Xiaoguang Zou
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Frontiers in Public Health, Vol 10 (2022)
Druh dokumentu: article
ISSN: 2296-2565
DOI: 10.3389/fpubh.2022.881234
Popis: ObjectiveBased on the respiratory disease big data platform in southern Xinjiang, we established a model that predicted and diagnosed chronic obstructive pulmonary disease, bronchiectasis, pulmonary embolism and pulmonary tuberculosis, and provided assistance for primary physicians.MethodsThe method combined convolutional neural network (CNN) and long-short-term memory network (LSTM) for prediction and diagnosis of respiratory diseases. We collected the medical records of inpatients in the respiratory department, including: chief complaint, history of present illness, and chest computed tomography. Pre-processing of clinical records with “jieba” word segmentation module, and the Bidirectional Encoder Representation from Transformers (BERT) model was used to perform word vectorization on the text. The partial and total information of the fused feature set was encoded by convolutional layers, while LSTM layers decoded the encoded information.ResultsThe precisions of traditional machine-learning, deep-learning methods and our proposed method were 0.6, 0.81, 0.89, and F1 scores were 0.6, 0.81, 0.88, respectively.ConclusionCompared with traditional machine learning and deep-learning methods that our proposed method had a significantly higher performance, and provided precise identification of respiratory disease.
Databáze: Directory of Open Access Journals