ArticleAmerican journal of translational research2025
Value of using blood routine indicators combined with artificial intelligence in sepsis patients.
Article in American journal of translational research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Closed-Loop digital therapeutics empowered by deep reinforcement learning and wearable sensing for precision orthopedic rehabilitation: a simulation-based proof-of-concept study.Frontiers in rehabilitation sciences · 2026Review
- Artificial intelligence-assisted phenotyping of sepsis: Research progress, clinical challenges, and translational prospects.Digital healthReview
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo 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.
methodsA 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.
resultsThe 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%.
conclusionCombining 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.
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