Semi-automated surveillance of surgical site infections using machine learning and rule-based classification models

Abstract

Surgical site infections (SSIs), among the most frequent healthcare-associated infections, require surveillance, but traditional methods are labour-intensive. We developed machine learning (ML) and rule-based models for the semi-automated detection of deep and organ/space SSIs using data from a prospective cohort of 3931 surgical patients. We assessed sensitivity and workload reduction (proportion of patients not requiring manual review) at a 0.5 decision threshold, and computed area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). The best-performing ML models (Naïve Bayes and dense neural network) achieved sensitivity up to 0.90, AUROC up to 0.968, AUPRC up to 0.248, and workload reduction over 90%. The rule-based model showed higher sensitivity (0.954) but lower AUROC, AUPRC, and workload reduction. Our findings suggest that semi-automated approaches can support efficient and accurate SSI surveillance while reducing manual workload. Further validation in other settings is warranted.

Fig. 1. SHAP values of the Naïve Bayes model (Bernoulli assumption).

The barplot (left), shows the average magnitude of SHAP values, ranking features by their overall influence. The beeswarm plot (right), provides a detailed view of each feature’s SHAP value distribution, indicating how high or low values affect the model’s output and highlighting feature interactions.

Fig. 2. SHAP values of the dense neural network model.

The barplot (left), shows the average magnitude of SHAP values, ranking features by their overall influence. The beeswarm plot (right), provides a detailed view of each feature’s SHAP value distribution, indicating how high or low values affect the model’s output and highlighting feature interactions.