ReviewAnnals of medicine and surgery (2012)2022
Performance of machine learning algorithms for surgical site infection case detection and prediction: A systematic review and meta-analysis.
Review in Annals of medicine and surgery (2012), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Systematic evaluation of machine learning models for postoperative surgical site infection prediction.PloS one · 2024Pooled it
- Explainable Machine Learning Predictive Models for Surgical Site Infections: Scoping Review.Journal of medical Internet research · 2026Article
- Systematic reviews of medical machine learning: limitations of pooling AUCs.European heart journal. Digital health · 2026Article
- Machine learning prediction model for surgical site infections after major abdominal surgery.Patient safety in surgery · 2026Article
- Development, Validation, and Comparison of Machine Learning Models for Predicting Pediatric Surgical Site Infections Using the NSQIP-P Database.Journal of the American College of Surgeons · 2026Article
- Explainable machine learning for perioperative surgical site infection risk enrichment after operative treatment of closed pilon fractures: a multicenter retrospective study with external validation.Frontiers in surgery · 2026Article
- A comprehensive feature importance analysis of surgical site infection following colorectal cancer surgery.Scientific reports · 2025Article
- Semi-automated surveillance of surgical site infections using machine learning and rule-based classification models.NPJ digital medicine · 2025Article
- Review
- Navigating the Transformative Impact of Artificial Intelligence in Health Services Research.Health science reports · 2025Article
- The Transformative Role of Artificial Intelligence in Plastic and Reconstructive Surgery: Challenges and Opportunities.Journal of clinical medicine · 2025Review
- Development and validation of artificial intelligence models for early detection of postoperative infections (PERISCOPE): a multicentre study using electronic health record data.The Lancet regional health. Europe · 2025Article
- Artificial intelligence for posterior capsule opacification.Frontiers in medicine · 2025Review
- MIASurviveMTP: Machine learning for immediate assessment and survival prediction after massive transfusion protocol.PloS one · 2025Article
- Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review.JMIR medical informatics · 2024Article
- An explainable long short-term memory network for surgical site infection identification.Surgery · 2024Article
- Machine Learning Approaches for the Image-Based Identification of Surgical Wound Infections: Scoping Review.Journal of medical Internet research · 2024Article
- Prediction of postoperative infectious complications in elderly patients with colorectal cancer: a study based on improved machine learning.BMC medical informatics and decision making · 2024Article
- The centre for health informatics: a novel approach to accelerating the field of health data science.International journal of population data science · 2024Article
- Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study.Antimicrobial resistance and infection control · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Medical researchers and clinicians have shown much interest in developing machine learning (ML) algorithms to detect/predict surgical site infections (SSIs). However, little is known about the overall performance of ML algorithms in predicting SSIs and how to improve the algorithm's robustness. We conducted a systematic review and meta-analysis to summarize the performance of ML algorithms in SSIs case detection and prediction and to describe the impact of using unstructured and textual data in the development of ML algorithms. Methods: MEDLINE, EMBASE, CINAHL, CENTRAL and Web of Science were searched from inception to March 25, 2021. Study characteristics and algorithm development information were extracted. Performance statistics (e.g., sensitivity, area under the receiver operating characteristic curve [AUC]) were pooled using a random effect model. Stratified analysis was applied to different study characteristic levels. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Diagnostic Test Accuracy Studies (PRISMA-DTA) was followed. Results: Of 945 articles identified, 108 algorithms from 32 articles were included in this review. The overall pooled estimate of the SSI incidence rate was 3.67%, 95% CI: 3.58-3.76. Mixed-use of structured and textual data-based algorithms (pooled estimates of sensitivity 0.83, 95% CI: 0.78-0.87, specificity 0.92, 95% CI: 0.86-0.95, AUC 0.92, 95% CI: 0.89-0.94) outperformed algorithms solely based on structured data (sensitivity 0.56, 95% CI:0.43-0.69, specificity 0.95, 95% CI:0.91-0.97, AUC = 0.90, 95% CI: 0.87-0.92). Conclusions: ML algorithms developed with structured and textual data provided optimal performance. External validation of ML algorithms is needed to translate current knowledge into clinical practice.
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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.