Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis
Abstract Here we propose CovSF, a deep learning model designed to track and forecast short-term severity progression of COVID-19 patients using longitudinal clinical records. The motivation stems from the need for timely medical resource allocation, improved treatment decisions during pandemics, and...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-07793-x |
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| author | Seung Hwan Bae Donghee Kim Jaeyeon Jang A-Sol Kim Soyoon Hwang Eunkyung Nam Sohyun Bae Ji Yeon Lee Ji Sun Kim Sang Cheol Kim Hye-Yeong Jo Kwangsoo Kim Inuk Jung Ki Tae Kwon |
| author_facet | Seung Hwan Bae Donghee Kim Jaeyeon Jang A-Sol Kim Soyoon Hwang Eunkyung Nam Sohyun Bae Ji Yeon Lee Ji Sun Kim Sang Cheol Kim Hye-Yeong Jo Kwangsoo Kim Inuk Jung Ki Tae Kwon |
| author_sort | Seung Hwan Bae |
| collection | DOAJ |
| description | Abstract Here we propose CovSF, a deep learning model designed to track and forecast short-term severity progression of COVID-19 patients using longitudinal clinical records. The motivation stems from the need for timely medical resource allocation, improved treatment decisions during pandemics, and the understanding of severity progression related immunology. The COVID-19 Severity Forecasting model, CovSF, utilizes 15 clinical features to profile the severity levels of hospital admitted patients and also forecast their severity levels of up to three days ahead. CovSF was trained on a large COVID-19 cohort (n=4,509), achieving an AUROC of 0.92 with 0.85 and 0.89 sensitivity and specificity on an external validation dataset (n=443). The type of oxygen therapy administered was utilized as the target predictive label, which is often used as the severity index. This approach enables the inclusion of a more comprehensive dataset encompassing patients across the full spectrum of severity, rather than restricting the analysis to more narrowly defined outcomes such as ICU admission or mortality. We focused on profiling deteriorating and recovering health conditions, which were validated using patient matched single-cell transcriptomes. Especially, we showed that the immunology significantly differed between the samples during deterioration and recovery, whose severity levels were the same, and thus presenting the importance of longitudinal analysis. We believe that the framework of CovSF can be extended to other respiratory infectious diseases to alleviate the strain of allocating hospital resources, especially in pandemics. |
| format | Article |
| id | doaj-art-ded32ff43c144638bc22a0c7783009e2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ded32ff43c144638bc22a0c7783009e22025-08-20T04:01:24ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-07793-xProfiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysisSeung Hwan Bae0Donghee Kim1Jaeyeon Jang2A-Sol Kim3Soyoon Hwang4Eunkyung Nam5Sohyun Bae6Ji Yeon Lee7Ji Sun Kim8Sang Cheol Kim9Hye-Yeong Jo10Kwangsoo Kim11Inuk Jung12Ki Tae Kwon13School of Computer Science and Engineering, Kyungpook National UniversitySeoul National University Hospital Biomedical Research InstituteSchool of Computer Science and Engineering, Kyungpook National UniversityDepartment of Family Medicine, Kyungpook National University Chilgok Hospital, School of Medicine, Kyungpook National UniversityDivision of Infectious Diseases, Department of Internal Medicine, Kyungpook National University Chilgok Hospital, School of Medicine, Kyungpook National UniversityDivision of Infectious Diseases, Department of Internal Medicine, Kyungpook National University Chilgok Hospital, School of Medicine, Kyungpook National UniversityDivision of Infectious Diseases, Department of Internal Medicine, School of Medicine, Kyungpook National University Hospital, Kyungpook National UniversityDivision of Infectious Diseases, Department of Internal Medicine, Keimyung University Dongsan Hospital, Keimyung University School of MedicineDepartment of Medical Information, Kyungpook National University HospitalDivision of Healthcare and Artificial Intelligence, Department of Precision Medicine, Korea National Institute of Health, Korea Disease Control and Prevention AgencyDivision of Healthcare and Artificial Intelligence, Department of Precision Medicine, Korea National Institute of Health, Korea Disease Control and Prevention AgencyDepartment of Transdisciplinary Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University HospitalSchool of Computer Science and Engineering, Kyungpook National UniversityDivision of Infectious Diseases, Department of Internal Medicine, Kyungpook National University Chilgok Hospital, School of Medicine, Kyungpook National UniversityAbstract Here we propose CovSF, a deep learning model designed to track and forecast short-term severity progression of COVID-19 patients using longitudinal clinical records. The motivation stems from the need for timely medical resource allocation, improved treatment decisions during pandemics, and the understanding of severity progression related immunology. The COVID-19 Severity Forecasting model, CovSF, utilizes 15 clinical features to profile the severity levels of hospital admitted patients and also forecast their severity levels of up to three days ahead. CovSF was trained on a large COVID-19 cohort (n=4,509), achieving an AUROC of 0.92 with 0.85 and 0.89 sensitivity and specificity on an external validation dataset (n=443). The type of oxygen therapy administered was utilized as the target predictive label, which is often used as the severity index. This approach enables the inclusion of a more comprehensive dataset encompassing patients across the full spectrum of severity, rather than restricting the analysis to more narrowly defined outcomes such as ICU admission or mortality. We focused on profiling deteriorating and recovering health conditions, which were validated using patient matched single-cell transcriptomes. Especially, we showed that the immunology significantly differed between the samples during deterioration and recovery, whose severity levels were the same, and thus presenting the importance of longitudinal analysis. We believe that the framework of CovSF can be extended to other respiratory infectious diseases to alleviate the strain of allocating hospital resources, especially in pandemics.https://doi.org/10.1038/s41598-025-07793-xSeverityProgressionTime courseDeep learningSingle-cell |
| spellingShingle | Seung Hwan Bae Donghee Kim Jaeyeon Jang A-Sol Kim Soyoon Hwang Eunkyung Nam Sohyun Bae Ji Yeon Lee Ji Sun Kim Sang Cheol Kim Hye-Yeong Jo Kwangsoo Kim Inuk Jung Ki Tae Kwon Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis Scientific Reports Severity Progression Time course Deep learning Single-cell |
| title | Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis |
| title_full | Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis |
| title_fullStr | Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis |
| title_full_unstemmed | Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis |
| title_short | Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis |
| title_sort | profiling short term longitudinal severity progression and associated genes in covid 19 patients using ehr and single cell analysis |
| topic | Severity Progression Time course Deep learning Single-cell |
| url | https://doi.org/10.1038/s41598-025-07793-x |
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