Prediction and Diagnosis of Respiratory Disease by Combining Convolutional Neural Network and Bi-directional Long Short-Term Memory Methods
Bibliographic Data
| ID | 22088870 |
|---|---|
| Authors | Li Li (0000-0001-9700-9682, Xinjiang Medical University), Ayiguli Alimu, Alimu Ayiguli, Qiyun Luan, Boyi Yang (0009-0002-0348-6643, Sun Yat-sen University), Subinuer Yilamujiang, Yilamujiang Subinuer, Hui Gong (0000-0002-7833-433X), Abudureherman Zulipikaer, Jingran Xu (0000-0002-8182-3927), Xuemei Zhong (0009-0001-3274-9759), Jiangtao Ren (0000-0003-2827-8322, Sun Yat-sen University, corresponding author), Xiaoguang Zou (0000-0002-9931-9251, corresponding author) |
| Year | 2022 |
| Volume | 10 |
| Pages | 881234-881234 |
| Publication date | 2022-05-04 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2022.881234 |
| PMID | 35602136 |
| OpenAlex | W4225426780 |
| Language | EN |
| Citations received | 2 |
| References cited | 33 |
Objective: Based 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. Methods: The 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. Results: 1 scores were 0.6, 0.81, 0.88, respectively. Conclusion: Compared with traditional machine learning and deep-learning methods that our proposed method had a significantly higher performance, and provided precise identification of respiratory disease
Bronchiectasis · Convolutional neural network · Deep learning · Feature learning · Lung · Speech recognition · Computer Science · COVID-19 diagnosis using AI · Machine Learning in Healthcare · Medicine · Phonocardiography and Auscultation Techniques · Artificial Intelligence · Internal Medicine
| Unique citing works | 2 |
|---|---|
| Citations per year | 1 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |