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<p>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 (1.000) 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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月17日

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  • Américo Agostinho,
  • Etienne Chalot,
  • Daniel Teixeira,
  • Davide Bosetti,
  • Niccolò Buetti,
  • Gaud Catho,
  • Stephan Harbarth,
  • Mohamed Abbas

摘要

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 (1.000) 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.