ArticleAnnals of medicine2025
Development of a machine learning prognostic model for early prediction of scrub typhus progression at hospital admission based on clinical and laboratory features.
Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Clinical characteristics and an admission-time predictive model for severe scrub typhus and secondary HLH in adults.BMC infectious diseases · 2026Article
- Analysis of the Epidemiological Characteristics and Risk Factors for Severe Progression of Scrub Typhus in the Dali Region of China.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Application of artificial intelligence models in the identification of severe scrub typhus.Frontiers in artificial intelligence · 2026Article
- Development and validation of machine learning-based prediction for in-hospital mortality in ICU patients with severe community-acquired pneumonia and respiratory failure.Frontiers in medicine · 2026Article
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Authors and funding
12 authors.
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Abstract
backgroundScrub typhus (ST) is a life-threatening infectious disease caused by
methodsThis study retrospectively collected severe and mild ST cases in two hospitals in Fujian Province, China from 2011 to 2022. Eighteen objective clinical and laboratory features collected at admission were screened using various feature selection algorithms, and used to construct models based on six machine learning algorithms.
resultsThe model based on Gradient Boosting Decision Tree using 14 features screened by Recursive Feature Elimination was evaluated as the optimal one. The model showed high accuracy, precision, sensitivity, specificity, F-1 score, and area under receiver operating characteristics curve of 0.975, 0.967, 0.983, 0.966, 0.975, and 0.981, respectively, indicating its possible clinical application. Additionally, a simplified model based on Support Vector Machine was constructed and evaluated as an alternative optimal model.
conclusionsThis study is the first to use machine learning algorithms to accurately predict the developments of ST patients upon admission to hospitals. The models can help clinicians assess the potential risks of their patients early on, thereby improving patient outcomes.
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