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An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19
A high-performing interpretable model is proposed to predict the risk of deterioration in coronavirus disease 2019 (COVID-19) patients. The model was developed using a cohort of 3028 patients diagnosed with COVID-19 and exhibiting common clinical symptoms that were internally verified (AUC 0.8517, 9...
| Autores principales: | , , , , , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633326/ https://www.ncbi.nlm.nih.gov/pubmed/34848736 http://dx.doi.org/10.1038/s41598-021-02370-4 |
| _version_ | 1784607904541704192 |
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| author | Jia, Lijing Wei, Zijian Zhang, Heng Wang, Jiaming Jia, Ruiqi Zhou, Manhong Li, Xueyan Zhang, Hankun Chen, Xuedong Yu, Zheyuan Wang, Zhaohong Li, Xiucheng Li, Tingting Liu, Xiangge Liu, Pei Chen, Wei Li, Jing He, Kunlun |
| author_facet | Jia, Lijing Wei, Zijian Zhang, Heng Wang, Jiaming Jia, Ruiqi Zhou, Manhong Li, Xueyan Zhang, Hankun Chen, Xuedong Yu, Zheyuan Wang, Zhaohong Li, Xiucheng Li, Tingting Liu, Xiangge Liu, Pei Chen, Wei Li, Jing He, Kunlun |
| author_sort | Jia, Lijing |
| collection | PubMed |
| description | A high-performing interpretable model is proposed to predict the risk of deterioration in coronavirus disease 2019 (COVID-19) patients. The model was developed using a cohort of 3028 patients diagnosed with COVID-19 and exhibiting common clinical symptoms that were internally verified (AUC 0.8517, 95% CI 0.8433, 0.8601). A total of 15 high risk factors for deterioration and their approximate warning ranges were identified. This included prothrombin time (PT), prothrombin activity, lactate dehydrogenase, international normalized ratio, heart rate, body-mass index (BMI), D-dimer, creatine kinase, hematocrit, urine specific gravity, magnesium, globulin, activated partial thromboplastin time, lymphocyte count (L%), and platelet count. Four of these indicators (PT, heart rate, BMI, HCT) and comorbidities were selected for a streamlined combination of indicators to produce faster results. The resulting model showed good predictive performance (AUC 0.7941 95% CI 0.7926, 0.8151). A website for quick pre-screening online was also developed as part of the study. |
| format | Online Article Text |
| id | pubmed-8633326 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2021 |
| publisher | Nature Publishing Group UK |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-86333262021-12-03 An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19 Jia, Lijing Wei, Zijian Zhang, Heng Wang, Jiaming Jia, Ruiqi Zhou, Manhong Li, Xueyan Zhang, Hankun Chen, Xuedong Yu, Zheyuan Wang, Zhaohong Li, Xiucheng Li, Tingting Liu, Xiangge Liu, Pei Chen, Wei Li, Jing He, Kunlun Sci Rep Article A high-performing interpretable model is proposed to predict the risk of deterioration in coronavirus disease 2019 (COVID-19) patients. The model was developed using a cohort of 3028 patients diagnosed with COVID-19 and exhibiting common clinical symptoms that were internally verified (AUC 0.8517, 95% CI 0.8433, 0.8601). A total of 15 high risk factors for deterioration and their approximate warning ranges were identified. This included prothrombin time (PT), prothrombin activity, lactate dehydrogenase, international normalized ratio, heart rate, body-mass index (BMI), D-dimer, creatine kinase, hematocrit, urine specific gravity, magnesium, globulin, activated partial thromboplastin time, lymphocyte count (L%), and platelet count. Four of these indicators (PT, heart rate, BMI, HCT) and comorbidities were selected for a streamlined combination of indicators to produce faster results. The resulting model showed good predictive performance (AUC 0.7941 95% CI 0.7926, 0.8151). A website for quick pre-screening online was also developed as part of the study. Nature Publishing Group UK 2021-11-30 /pmc/articles/PMC8633326/ /pubmed/34848736 http://dx.doi.org/10.1038/s41598-021-02370-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
| spellingShingle | Article Jia, Lijing Wei, Zijian Zhang, Heng Wang, Jiaming Jia, Ruiqi Zhou, Manhong Li, Xueyan Zhang, Hankun Chen, Xuedong Yu, Zheyuan Wang, Zhaohong Li, Xiucheng Li, Tingting Liu, Xiangge Liu, Pei Chen, Wei Li, Jing He, Kunlun An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19 |
| title | An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19 |
| title_full | An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19 |
| title_fullStr | An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19 |
| title_full_unstemmed | An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19 |
| title_short | An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19 |
| title_sort | interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for covid-19 |
| topic | Article |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633326/ https://www.ncbi.nlm.nih.gov/pubmed/34848736 http://dx.doi.org/10.1038/s41598-021-02370-4 |
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