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https://dspace.ncfu.ru/handle/20.500.12258/18093
Title: | Pulmonary fibrosis progression prognosis using machine learning |
Authors: | Glotov, A. S. Глотов, А. С. Lyakhov, P. A. Ляхов, П. А. |
Keywords: | Computer-aided diagnostics;Machine learning;Pulmonary fibrosis progression prognosis;Biomedical engineering |
Issue Date: | 2021 |
Publisher: | Institute of Electrical and Electronics Engineers Inc. |
Citation: | Glotov, A. S.; Lyakhov, P. A. Pulmonary fibrosis progression prognosis using machine learning // Proceedings - 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021. - 2021. - Стр.: 327 - 329. - Номер статьи 9455070 |
Series/Report no.: | Proceedings - 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021 |
Abstract: | Lung fibrosis means scarring of tissue in a patient's lungs and is a common condition that can complicate the course of COVID-19 disease. Pulmonary fibrosis destroys the patient's lungs, preventing oxygenation of the blood. Modern methods of treatment are not highly effective even with access to a patient's CT scan. The problem of predicting the prognosis of pulmonary fibrosis is extremely important, since its solution will make it possible to organize clinical trials to study methods of treating patients with fibrosis more effectively. This article proposes a method for predicting the prognosis of pulmonary fibrosis progression as the volume of inhaled and exhaled air to the individual patient based on tabular patient data using an ensemble of four machine learning algorithms. This solution also provides a forecast accuracy because it is useful in medical applications to assess the 'confidence' of the model in its predictions. Modeling the proposed method shows a better result than other forecasting methods that are compared in the article. Keywords-Pulmonary fibrosis progression prognosis, Machine learning, Computer-aided diagnostics |
URI: | http://hdl.handle.net/20.500.12258/18093 |
Appears in Collections: | Статьи, проиндексированные в SCOPUS, WOS |
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scopusresults 1856 .pdf Restricted Access | 1.79 MB | Adobe PDF | View/Open |
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