ArticleJournal of the American Medical Informatics Association : JAMIA2018
Identifying surgical site infections in electronic health data using predictive models.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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Who cites it
11 citing papers in PubMed, 19 citations in OpenAlex.
- Automated Identification of Surgical Site Infections From Electronic Medical Records: Retrospective Observational Predictive Modeling Study.JMIR perioperative medicine · 2026Observational
- 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
- Development and Evaluation of Machine Learning Models for the Identification of Surgical Site Infection in Electronic Health Records.Surgical infections · 2025Article
- Automated extraction of standardized antibiotic resistance and prescription data from laboratory information systems and electronic health records: a narrative review.Frontiers in antibiotics · 2024Review
- The augmented value of using clinical notes in semi-automated surveillance of deep surgical site infections after colorectal surgery.Antimicrobial resistance and infection control · 2023Observational
- Strategies to prevent surgical site infections in acute-care hospitals: 2022 Update.Infection control and hospital epidemiology · 2023Article
- Performance of machine learning algorithms for surgical site infection case detection and prediction: A systematic review and meta-analysis.Annals of medicine and surgery (2012) · 2022Review
- A novel model to label delirium in an intensive care unit from clinician actions.BMC medical informatics and decision making · 2021Article
- Artificial Intelligence-Based Multimodal Risk Assessment Model for Surgical Site Infection (AMRAMS): Development and Validation Study.JMIR medical informatics · 2020Article
- Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- User Testing an Information Foraging Tool for Ambulatory Surgical Site Infection Surveillance.Applied clinical informatics · 2018Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Objective: The objective was to prospectively derive and validate a prediction rule for detecting cases warranting investigation for surgical site infections (SSI) after ambulatory surgery. Methods: We analysed electronic health record (EHR) data for children who underwent ambulatory surgery at one of 4 ambulatory surgical facilities. Using regularized logistic regression and random forests, we derived SSI prediction rules using 30 months of data (derivation set) and evaluated performance with data from the subsequent 10 months (validation set). Models were developed both with and without data extracted from free text. We also evaluated the presence of an antibiotic prescription within 60 days after surgery as an independent indicator of SSI evidence. Our goal was to exceed 80% sensitivity and 10% positive predictive value (PPV). Results: We identified 234 surgeries with evidence of SSI among the 7910 surgeries available for analysis. We derived and validated an optimal prediction rule that included free text data using a random forest model (sensitivity = 0.9, PPV = 0.28). Presence of an antibiotic prescription had poor sensitivity (0.65) when applied to the derivation data but performed better when applied to the validation data (sensitivity = 0.84, PPV = 0.28). Conclusions: EHR data can facilitate SSI surveillance with adequate sensitivity and PPV.
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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.