ArticleBiomedical engineering online2025
A KNN-based model for non-invasive prediction of hemorrhagic shock severity in prehospital settings: integrating MAP, P
Article in Biomedical engineering online, 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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7 authors.
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Abstract
backgroundRapid prehospital assessment of hemorrhagic shock severity is critical for trauma triage and intervention. Current non-invasive single-parameter monitoring shows limited diagnostic reliability. We developed a multi-parameter predictive model integrating mean arterial pressure (MAP), buccal mucosal CO₂ (P
methodsForty-five Wistar rats were randomly divided into five groups (n = 9) with different blood loss amounts. MAP, P
resultsWhen the training vs testing data set ratio is 7:3 or 6:4, and K = 3, the KNN-based model has the best prediction accuracy (94.82% and 93.51%). The confusion matrix and ROC evaluation showed that the overall performance of the KNN-based model is superior to that of the SVM-based model, at all levels of blood loss (F1 = 95.09% and 93.99%, AUC = 1 and 0.97 for the KNN-based model at 7:3 and 6:4 dataset ratio; F1 = 83.84% and 84.86%, AUC = 0.97 and 0.97 for the SVM-based model at 7:3 and 6:4 dataset ratio).
conclusionsUsing the detection indicators MAP, P
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