ArticleFrontiers in microbiology2025
Machine learning-based high-specificity diagnostic model for
Article in Frontiers in microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study developed and validated a machine learning (ML)-based predictive model utilizing febrile patients' routine clinical laboratory data for the purpose of screening such patients for Methods: This retrospective study enrolled febrile patients who visited Zhejiang Provincial People's Hospital and the Third Affiliated Hospital of Zhejiang Chinese Medical University from January 2021-April 2025. Patient data, including sex, age, and laboratory test results, were collected. Through sparse partial least squares discriminant analysis, the most informative features were extracted from the dataset. Six classic machine learning algorithms were utilized to develop the optimal predictive model through 1000 bootstrap resamplings. Finally, the model was validated on an independent clinical validation dataset. Results: The training dataset comprised 485 febrile patients (141 with Conclusion: A random forest model can effectively utilize routine clinical laboratory data to predict
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