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. 2025 Apr 15;17(4):2678-2689.
doi: 10.62347/MMNQ1017. eCollection 2025.

Value of using blood routine indicators combined with artificial intelligence in sepsis patients

Jianhui Chen  1 , Minghuan Huang  2 , Rongbin Xu  3 , Yanya Lin  1 , Shijun Chen  1 , Ying Xie  3 , Jianxiong Hu  1
Affiliations

Value of using blood routine indicators combined with artificial intelligence in sepsis patients

Jianhui Chen et al. Am J Transl Res. .

Abstract

Objective: To explore the correlation between Blood Routine Indicators (BRI) and sepsis using machine learning algorithms (MLAs) and evaluate their application in early sepsis for prognosis assessment.

Methods: A total of 4,558 blood routine data (BRD) samples were collected, including 149 sepsis patients and 186 patients with common infections (CI). A binary logistic regression model (BLRM) was constructed to predict sepsis based on BRI. Additionally, MLAs were applied, including support vector machines, neural networks, Bayesian classifiers, k-nearest neighbors), decision trees, and random forest classification models (RFCM). The performance of these seven predictive models was evaluated.

Results: The RFCM demonstrated the best predictive performance among the MLAs, with accuracy of 86.97%, precision of 87.02%, recall of 86.97%, and F1 score of 0.87. These metrics were significantly higher than those of the BLRM (accuracy: 68.77%, precision PRE: 71.45%, recall: 69.47%, F1 Score: 0.70). In the random forest model, red blood cell volume distribution width (RDW) was identified as the most significant feature, with RDW-coefficient of variation contributing 6.98% and RDW-standard deviation contributing 5.32%.

Conclusion: Combining blood routine indicators (BRI) with MLA has considerable potential in predicting sepsis. The RFCM showed the highest predictive value, and RDW may play a crucial role in sepsis prediction.

Keywords: Sepsi; blood routine; machine learning algorithms; prediction.

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Conflict of interest statement

None.

Figures

Figure 1
Figure 1
A perplexing matrix depicting the test set results of various machine learning algorithm models. A: Random forest classification model; B: Bayesian classification model; C: Decision tree classification model; D: KNN classification model; E: Support vector model; F: Neural network model.
Figure 2
Figure 2
ROC curves of predictive models. A: KNN model; B: Bayesian model; C: Decision tree model; D: Neural network model; E: Random forest model; F: Support vector model; G: Logistic regression model.
Figure 3
Figure 3
Weighted values of blood conventional indicators by random forest models.
See this image and copyright information in PMC

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