A machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome: a prospective, multicenter cohort study
Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease that imposes a considerable medical burden. In this study, we enrolled 1,606 SFTS patients, developed and validated machine learning models for mortality prediction, and ultimately constructed a model consisting of...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Emerging Microbes and Infections |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22221751.2025.2498572 |
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| author | Yanan Liu Lei Fan Wencai Wang Hongxuan Song Zhenhua Zhang Qian Liu Zhongji Meng Shibo Li Hua Wang Shijun Zhou Wanjun Liu Guomei Xia Jianping Duan Chunxia Guo Lu Wang Ling Xu Tong Wang Hanxin Li Xinyue Zhang Tiandan Xiang Di Liu Zujiang Yu Yuliang Liu Junzhong Wang Xin Zheng |
| author_facet | Yanan Liu Lei Fan Wencai Wang Hongxuan Song Zhenhua Zhang Qian Liu Zhongji Meng Shibo Li Hua Wang Shijun Zhou Wanjun Liu Guomei Xia Jianping Duan Chunxia Guo Lu Wang Ling Xu Tong Wang Hanxin Li Xinyue Zhang Tiandan Xiang Di Liu Zujiang Yu Yuliang Liu Junzhong Wang Xin Zheng |
| author_sort | Yanan Liu |
| collection | DOAJ |
| description | Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease that imposes a considerable medical burden. In this study, we enrolled 1,606 SFTS patients, developed and validated machine learning models for mortality prediction, and ultimately constructed a model consisting of six variables. The prediction model, UNION-SFTS, constructed using the multilayer perceptron (MLP) algorithm, achieved the best performance with an area under the curve (AUC) of 0.917, an accuracy of 0.905, and a precision of 0.795 on the internal validation set. Additionally, the model achieved an AUC of 0.883 on the prospective validation set and AUCs of 1.000, 0.927 and 0.905 on the three external validation sets, respectively. We developed a user-friendly web-based calculator for clinical use, available at http://175.178.66.58/english/. By utilizing the UNION-SFTS model, clinicians can promptly predict and monitor the disease severity and mortality risk of SFTS patients, enabling early intervention in severe cases and ultimately reduces patient mortality. |
| format | Article |
| id | doaj-art-925c4889591d4f10a1cf5e80100b3d8f |
| institution | Kabale University |
| issn | 2222-1751 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Emerging Microbes and Infections |
| spelling | doaj-art-925c4889591d4f10a1cf5e80100b3d8f2025-08-20T03:28:17ZengTaylor & Francis GroupEmerging Microbes and Infections2222-17512025-12-0114110.1080/22221751.2025.2498572A machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome: a prospective, multicenter cohort studyYanan Liu0Lei Fan1Wencai Wang2Hongxuan Song3Zhenhua Zhang4Qian Liu5Zhongji Meng6Shibo Li7Hua Wang8Shijun Zhou9Wanjun Liu10Guomei Xia11Jianping Duan12Chunxia Guo13Lu Wang14Ling Xu15Tong Wang16Hanxin Li17Xinyue Zhang18Tiandan Xiang19Di Liu20Zujiang Yu21Yuliang Liu22Junzhong Wang23Xin Zheng24Department of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaDepartment of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaDepartment of Infectious Diseases, The Sixth People's Hospital of Qingdao City, Qingdao, People’s Republic of ChinaDepartment of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Anhui, People’s Republic of ChinaDepartment of Infectious Diseases, No.1 People's Hospital of Guangshui, Hubei, People’s Republic of ChinaDepartment of Infectious Diseases, Hubei Provincial Clinical Research Center for Precise Diagnosis and Treatment of Liver Cancer, Taihe Hospital, Hubei University of Medicine, Hubei, People's Republic of ChinaDepartment of Infectious Diseases, Zhoushan Hospital, Wenzhou Medical University, Zhoushan, People’s Republic of ChinaDepartment of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaDepartment of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Anhui, People’s Republic of ChinaDepartment of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Anhui, People’s Republic of ChinaDepartment of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Anhui, People’s Republic of ChinaDepartment of Infectious Diseases, The Sixth People's Hospital of Qingdao City, Qingdao, People’s Republic of ChinaDepartment of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaDepartment of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaDepartment of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaDepartment of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaDepartment of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaDepartment of Infectious Diseases, The First Affiliated Hospital of Soochow University, Suzhou, People’s Republic of ChinaPritzker School of Medicine, University of Chicago, Chicago, IL, USADepartment of Infectious Diseases, State Key Laboratory of Antiviral Drugs, Pingyuan Laboratory, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of ChinaSchool of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaDepartment of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaDepartment of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaSevere fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease that imposes a considerable medical burden. In this study, we enrolled 1,606 SFTS patients, developed and validated machine learning models for mortality prediction, and ultimately constructed a model consisting of six variables. The prediction model, UNION-SFTS, constructed using the multilayer perceptron (MLP) algorithm, achieved the best performance with an area under the curve (AUC) of 0.917, an accuracy of 0.905, and a precision of 0.795 on the internal validation set. Additionally, the model achieved an AUC of 0.883 on the prospective validation set and AUCs of 1.000, 0.927 and 0.905 on the three external validation sets, respectively. We developed a user-friendly web-based calculator for clinical use, available at http://175.178.66.58/english/. By utilizing the UNION-SFTS model, clinicians can promptly predict and monitor the disease severity and mortality risk of SFTS patients, enabling early intervention in severe cases and ultimately reduces patient mortality.https://www.tandfonline.com/doi/10.1080/22221751.2025.2498572SFTSbandavirusearly warning systemrisk prediction modelmachine learning |
| spellingShingle | Yanan Liu Lei Fan Wencai Wang Hongxuan Song Zhenhua Zhang Qian Liu Zhongji Meng Shibo Li Hua Wang Shijun Zhou Wanjun Liu Guomei Xia Jianping Duan Chunxia Guo Lu Wang Ling Xu Tong Wang Hanxin Li Xinyue Zhang Tiandan Xiang Di Liu Zujiang Yu Yuliang Liu Junzhong Wang Xin Zheng A machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome: a prospective, multicenter cohort study Emerging Microbes and Infections SFTS bandavirus early warning system risk prediction model machine learning |
| title | A machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome: a prospective, multicenter cohort study |
| title_full | A machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome: a prospective, multicenter cohort study |
| title_fullStr | A machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome: a prospective, multicenter cohort study |
| title_full_unstemmed | A machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome: a prospective, multicenter cohort study |
| title_short | A machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome: a prospective, multicenter cohort study |
| title_sort | machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome a prospective multicenter cohort study |
| topic | SFTS bandavirus early warning system risk prediction model machine learning |
| url | https://www.tandfonline.com/doi/10.1080/22221751.2025.2498572 |
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