Author: Fang, Cong; Bai, Song; Chen, Qianlan; Zhou, Yu; Xia, Liming; Qin, Lixin; Gong, Shi; Xie, Xudong; Zhou, Chunhua; Tu, Dandan; Zhang, Changzheng; Liu, Xiaowu; Chen, Weiwei; Bai, Xiang; Torr, Philip H.S.
Title: Deep learning for predicting COVID-19 malignant progression Cord-id: 8qc9q7rp Document date: 2021_5_12
ID: 8qc9q7rp
Snippet: As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosis and treatment, which has greatly challenged public medical systems. Treatment priority is often determined by the symptom severity based on first assessment. However, clinical observation suggests that some patients with mild symptoms may quickly deteriorate. Hence, it is crucial to identify patient early deterioration to optimize treatment strategy. To this end, we develop an early-warning syst
Document: As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosis and treatment, which has greatly challenged public medical systems. Treatment priority is often determined by the symptom severity based on first assessment. However, clinical observation suggests that some patients with mild symptoms may quickly deteriorate. Hence, it is crucial to identify patient early deterioration to optimize treatment strategy. To this end, we develop an early-warning system with deep learning techniques to predict COVID-19 malignant progression. Our method leverages CT scans and the clinical data of outpatients and achieves an AUC of 0.920 in the single-center study. We also propose a domain adaptation approach to improve the generalization of our model and achieve an average AUC of 0.874 in the multicenter study. Moreover, our model automatically identifies crucial indicators that contribute to the malignant progression, including Troponin, Brain natriuretic peptide, White cell count, Aspartate aminotransferase, Creatinine, and Hypersensitive C-reactive protein.
Search related documents:
Co phrase search for related documents- absolute lasso selection shrinkage operator and accurate prediction: 1, 2, 3, 4
- absolute lasso selection shrinkage operator and long lstm short term memory: 1
- absolute lasso selection shrinkage operator and lstm short term memory: 1
- absolute lasso selection shrinkage operator and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21
- absolute lasso selection shrinkage operator and machine learning method: 1
- accurate model and adam optimizer: 1
- accurate model and long lstm short term memory: 1, 2, 3, 4, 5, 6, 7, 8, 9
- accurate model and long lstm short term memory network: 1, 2, 3, 4
- accurate model and loss function: 1, 2, 3, 4, 5, 6
- accurate model and lstm short term memory: 1, 2, 3, 4, 5, 6, 7, 8, 9
- accurate model and machine learn: 1
- accurate model and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61
- accurate model and machine learning method: 1, 2, 3, 4
- accurate prediction and long lstm short term memory: 1, 2, 3, 4, 5, 6, 7, 8, 9
- accurate prediction and long lstm short term memory network: 1, 2, 3
- accurate prediction and loss function: 1, 2
- accurate prediction and lstm short term memory: 1, 2, 3, 4, 5, 6, 7, 8, 9
- accurate prediction and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
- accurate prediction and machine learning method: 1, 2, 3, 4, 5, 6, 7, 8
Co phrase search for related documents, hyperlinks ordered by date