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 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.

Authors

Institutions

Publication Details

Journal
Emerging Microbes & Infections
Published
2025-05-01
DOI
https://doi.org/10.1080/22221751.2025.2498572
Citations
10
Primary Topic
Viral Infections and Vectors
Type
article
Field-Weighted Citation Impact
13.96

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome: a prospective, multicenter cohort study

Zhenghua Zhang, Junzhong Wang, Wanjun Liu, Zhongji Meng et al.
10 citations
Emerging Microbes & Infections
Viral Infections and Vectors
13.96
article

A machine learning model for mortality prediction in patients with severe fever with thrombocytopenia syndrome: a prospective, multicenter cohort study

Zhenghua Zhang, Junzhong Wang, Wanjun Liu, Zhongji Meng, Guomei Xia, Xin Zheng, Y Liu, Hongxuan Song, Ling Xu, Xinyue Zhang, Lu Wang, Yuliang Liu, Hua Wang, Huihui Li, Tong Wang, Shibo Li, Zujiang Yu, Wencai Wang, Chunxia Guo, Jianping Duan, Lei Fan, Tiandan Xiang, Shijun Zhou, Di Liu, Qian Liu
article en
10 citations

Abstract

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.

Emerging Microbes & InfectionsVol. 14(1)
Tongji University (CN), Hubei University of Medicine (CN), Anhui Medical University (CN), Wenzhou Medical University (CN), Soochow University (CN), University of Chicago (US), Hubei University of Arts and Science (CN), Taihe Hospital (CN), Xiangyang Central Hospital (CN), Zhoushan Hospital (CN), Infectious Clinical Hospital No. 1 (RU), Second Affiliated Hospital of Anhui Medical University (CN), First Affiliated Hospital of Soochow University (CN), First Affiliated Hospital of Zhengzhou University (CN), Qingdao Eighth People's Hospital (CN), Huazhong University of Science and Technology (CN)
National Natural Science Foundation of China
Good health and well-being
Openalex Percentile: Top 1%
Viral Infections and Vectors
13.96
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.