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

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.