An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19
© 2021. The Author(s)..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
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| Enthalten in: |
Scientific reports - 11(2021), 1 vom: 30. Nov., Seite 23127 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jia, Lijing [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 08.12.2021 Date Revised 04.04.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s41598-021-02370-4 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a © 2021. The Author(s). | ||
| 520 | |a 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 | ||
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| 700 | 1 | |a Zhang, Heng |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Jiaming |e verfasserin |4 aut | |
| 700 | 1 | |a Jia, Ruiqi |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Manhong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xueyan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Hankun |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Xuedong |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Zheyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Zhaohong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xiucheng |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Tingting |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Xiangge |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Pei |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a He, Kunlun |e verfasserin |4 aut | |
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