Prediction of viral pneumonia based on machine learning models analyzing pulmonary inflammation index scores.

Autor: Wang Y; Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China; Anesthesiology and Critical Care Medicine Key Laboratory of Luzhou, Southwest Medical University, Luzhou, Sichuan Province, 646000, China. Electronic address: 1050558184@qq.com., Liu ZL; Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China; Department of Intervention Radiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Lu Zhou, 646000, Sichuan, China. Electronic address: lzonglin96@163.com., Yang H; Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China. Electronic address: 375787621@qq.com., Li R; Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China. Electronic address: 1172182069@qq.com., Liao SJ; Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China. Electronic address: 1625825634@qq.com., Huang Y; Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China. Electronic address: hyao97@163.com., Peng MH; Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China. Electronic address: 1041336548@qq.com., Liu X; Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China. Electronic address: 63106235@qq.com., Si GY; Department of Intervention Radiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Lu Zhou, 646000, Sichuan, China. Electronic address: Siguangyan@swmu.edu.cn., He QZ; Department of Radiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China. Electronic address: lyhqz0806@163.com., Zhang Y; Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Luzhou, 646000, Sichuan, China. Electronic address: zhangying021210@163.com.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Feb; Vol. 169, pp. 107905. Date of Electronic Publication: 2023 Dec 25.
DOI: 10.1016/j.compbiomed.2023.107905
Abstrakt: Object: To obtain Pulmonary Inflammation Index scores from imaging chest CT and combine it with clinical correlates of viral pneumonia to predict the risk and severity of viral pneumonia using a computer learning model.
Methods: All patients with suspected viral pneumonia on CT examination admitted to The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University from December 2022 to March 2023 were retrospectively selected. The respiratory viruses were monitored by RT-PCR and categorized into patients with viral pneumonia and those with non-viral pneumonia. The extent of lung inflammation was quantified according to the Pulmonary Inflammation Index score (PII). Information on patient demographics, comorbidities, laboratory tests, pathogenetic testing, and radiological data were collected. Five machine learning models containing Random Forest(RF), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM), K Nearest Neighbour Algorithm (KNN), and Kernel Ridge Regression (KRR) were used to predict the risk of onset and severity of viral pneumonia based on the clinically relevant factors or PII.
Results: Among the five models, the SVM model performed best in ACC (76.75 %), SN (73.99 %), and F1 (72.42 %) and achieved a better area under the receiver operating characteristic curve (ROC) (0.8409) when predicting the risk of developing viral pneumonia. RF had the best overall classification accuracy in predicting the severity of viral pneumonia, especially in predicting pneumonia with a PII classification of grade I, the RF model achieved an accuracy of 98.89%.
Conclusion: Machine learning models are valuable in assessing the risk of viral pneumonia. Meanwhile, machine learning models confirm the importance in predicting the severity of viral pneumonia through PII. The establishment of machine learning models for predicting the risk and severity of viral pneumonia promotes the further development of machine learning in the medical field.
Competing Interests: Declaration of competing interest The authors declare that this study was conducted without any business or financial relationships that could be perceived as a potential conflict of interest.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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