Use of artificial intelligence to assess genetic predisposition to develop critical COVID-19 disease: a comparative study of machine learning models
Early prediction of critical COVID-19 disease is crucial for an optimal clinical management. The objective of this study was to optimize predictive models for critical COVID-19 disease. Clinical data, laboratory data and genetic polymorphisms were integrated into AI models to compare the performance...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-05-01
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| Series: | Advances in Laboratory Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/almed-2025-0073 |
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| author | Martín Pérez Salomón Sanchez Jimenez Flora Fuentes Cantero Sandra Jímenez Barragan Marta Sanchez Mora Catalina Borreguero Leon Juan M. Teresa Arrobas Velilla Valido Morales Agustín Delgado Torralbo Juan A. León Justel Antonio |
| author_facet | Martín Pérez Salomón Sanchez Jimenez Flora Fuentes Cantero Sandra Jímenez Barragan Marta Sanchez Mora Catalina Borreguero Leon Juan M. Teresa Arrobas Velilla Valido Morales Agustín Delgado Torralbo Juan A. León Justel Antonio |
| author_sort | Martín Pérez Salomón |
| collection | DOAJ |
| description | Early prediction of critical COVID-19 disease is crucial for an optimal clinical management. The objective of this study was to optimize predictive models for critical COVID-19 disease. Clinical data, laboratory data and genetic polymorphisms were integrated into AI models to compare the performance of different machine learning algorithms. |
| format | Article |
| id | doaj-art-15dc1428f2804d8e85d509ee69f29f0f |
| institution | OA Journals |
| issn | 2628-491X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Advances in Laboratory Medicine |
| spelling | doaj-art-15dc1428f2804d8e85d509ee69f29f0f2025-08-20T02:24:15ZengDe GruyterAdvances in Laboratory Medicine2628-491X2025-05-016218118910.1515/almed-2025-0073Use of artificial intelligence to assess genetic predisposition to develop critical COVID-19 disease: a comparative study of machine learning modelsMartín Pérez Salomón0Sanchez Jimenez Flora1Fuentes Cantero Sandra2Jímenez Barragan Marta3Sanchez Mora Catalina4Borreguero Leon Juan M.5Teresa Arrobas Velilla6Valido Morales Agustín7Delgado Torralbo Juan A.8León Justel Antonio916582Service of Clinical Biochemistry Virgen Macarena University Hospital Seville, Seville, Spain16582Service of Clinical Biochemistry Virgen Macarena University Hospital Seville, Seville, SpainDepartment of Clinical Laboratory Chemistry Rio Tinto General Hospital Huelva, Huelva, Spain16582Service of Clinical Biochemistry Virgen Macarena University Hospital Seville, Seville, Spain16582Service of Clinical Biochemistry Virgen Macarena University Hospital Seville, Seville, Spain16582Service of Clinical Biochemistry Virgen Macarena University Hospital Seville, Seville, Spain16582Service of Clinical Biochemistry Virgen Macarena University Hospital Seville, Seville, SpainUnit of Pulmonology, Virgen Macarena University Hospital Seville, Seville, SpainUnit of Pulmonology, Virgen Macarena University Hospital Seville, Seville, Spain16582Service of Clinical Biochemistry Virgen Macarena University Hospital Seville, Seville, SpainEarly prediction of critical COVID-19 disease is crucial for an optimal clinical management. The objective of this study was to optimize predictive models for critical COVID-19 disease. Clinical data, laboratory data and genetic polymorphisms were integrated into AI models to compare the performance of different machine learning algorithms.https://doi.org/10.1515/almed-2025-0073machine learningcovid-19critical diseaseartificial intelligencegenetic polymorphisms (snps)logistic regression |
| spellingShingle | Martín Pérez Salomón Sanchez Jimenez Flora Fuentes Cantero Sandra Jímenez Barragan Marta Sanchez Mora Catalina Borreguero Leon Juan M. Teresa Arrobas Velilla Valido Morales Agustín Delgado Torralbo Juan A. León Justel Antonio Use of artificial intelligence to assess genetic predisposition to develop critical COVID-19 disease: a comparative study of machine learning models Advances in Laboratory Medicine machine learning covid-19 critical disease artificial intelligence genetic polymorphisms (snps) logistic regression |
| title | Use of artificial intelligence to assess genetic predisposition to develop critical COVID-19 disease: a comparative study of machine learning models |
| title_full | Use of artificial intelligence to assess genetic predisposition to develop critical COVID-19 disease: a comparative study of machine learning models |
| title_fullStr | Use of artificial intelligence to assess genetic predisposition to develop critical COVID-19 disease: a comparative study of machine learning models |
| title_full_unstemmed | Use of artificial intelligence to assess genetic predisposition to develop critical COVID-19 disease: a comparative study of machine learning models |
| title_short | Use of artificial intelligence to assess genetic predisposition to develop critical COVID-19 disease: a comparative study of machine learning models |
| title_sort | use of artificial intelligence to assess genetic predisposition to develop critical covid 19 disease a comparative study of machine learning models |
| topic | machine learning covid-19 critical disease artificial intelligence genetic polymorphisms (snps) logistic regression |
| url | https://doi.org/10.1515/almed-2025-0073 |
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