ArticleNPJ digital medicine2025
Semi-automated surveillance of surgical site infections using machine learning and rule-based classification models.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Explainable Machine Learning Predictive Models for Surgical Site Infections: Scoping Review.Journal of medical Internet research · 2026Article
- Semi-Automated Retrospective Surveillance of Surgical Site Infections (sAReS-SSI) - using hospital routine data for SSI tracking.Antimicrobial resistance and infection control · 2026Article
- Machine learning prediction model for surgical site infections after major abdominal surgery.Patient safety in surgery · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.