Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Oct 22, 2024
Open Peer Review Period: Oct 22, 2024 - Dec 17, 2024
Date Accepted: Mar 24, 2025
(closed for review but you can still tweet)
Evaluation of a machine-learning model based on laboratory parameters for the prediction of influenza A and B: a multicenter model development and validation study in Chongqing, China
ABSTRACT
Background:
The influenza virus is a major pathogen causing acute respiratory infections (ARI) in humans, characterized by fever, cough, sore throat, muscle aches, and fatigue. Accurate identification of influenza A and B is crucial for effective treatment and reducing mortality.
Objective:
This study evaluates machine learning approaches and introduces an extreme gradient boosting model, optimized through a custom variable selection algorithm, to predict influenza subtypes based on routine laboratory parameters.
Methods:
This multicentre study used data from three independent hospitals databases. Patients aged 18 or older diagnosed with influenza A (A+ group), influenza B (B+ group), or those with influenza-like symptoms but testing negative for both (A-/B- group) between January 1, 2023, and May 31, 2024, were included. Model performance was evaluated using AUC, accuracy, specificity, sensitivity, PPV, NPV, and F1-score. The final tool, Artificial Intelligence Prediction of Influenza A and B (Ai-Lab) was developed from these models.
Results:
In the internal testing cohort, seven models (KNN, NB, DT, RF, XGB, GBDT, CATB) were evaluated. AUC values for diagnosing influenza A ranged from 0.865 to 0.971, and for influenza B from 0.815 to 0.961. The CATB-based Ai-Lab model, in the external validation cohort, achieved an accuracy of 86.8% for differentiating the A+ group from the A-/B- group and 85.6% for distinguishing the B+ group from the A-/B- group.
Conclusions:
Machine learning models, particularly the CATB-based Ai-Lab model, show promise in accurately predicting influenza A and B infections using routine laboratory parameters, potentially enhancing influenza diagnosis and management.
Citation
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Copyright
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