ArticleAnnals of surgery2020
Portable Automated Surveillance of Surgical Site Infections Using Natural Language Processing: Development and Validation.
Article in Annals of surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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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.
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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
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Natural language processing in dermatology: A systematic literature review and state of the art.Journal of the European Academy of Dermatology and Venereology : JEADV · 2024Pooled it
- Automated Identification of Surgical Site Infections From Electronic Medical Records: Retrospective Observational Predictive Modeling Study.JMIR perioperative medicine · 2026Observational
- Machine learning prediction model for surgical site infections after major abdominal surgery.Patient safety in surgery · 2026Article
- Identification and validation of respiratory virus immunization using natural language processing.Frontiers in digital health · 2026Article
- The Infectious Diseases Orchestrator: Embracing AI Literacy in the Agentic Era.Open forum infectious diseases · 2026Article
- Mapping antimicrobial resistance and healthcare-associated infections indicators for actionable benchmarking: a cross-network overview in a data-driven era.Antimicrobial resistance and infection control · 2025Review
- Methylxanthines: The Major Impact of Caffeine in Clinical Practice in Patients Diagnosed with Apnea of Prematurity.Journal of clinical medicine · 2025Review
- Towards an AI-driven registry for postoperative complications: a proof-of-concept study evaluating the opportunities and challenges of AI models.BMJ health & care informatics · 2025Article
- Surgical indicators for obstetrics and family planning in routine health information systems: a landscape analysis.Health policy and planning · 2025Article
- Development and Evaluation of Machine Learning Models for the Identification of Surgical Site Infection in Electronic Health Records.Surgical infections · 2025Article
- Artificial Intelligence and Postoperative Monitoring in Plastic Surgery.Plastic surgery (Oakville, Ont.) · 2025Review
- Advancing infection prevention and control through artificial intelligence: a scoping review of applications, barriers, and a decision-support checklist.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2025Article
- Recent technological advancements in Artificial Intelligence for orthopaedic wound management.Journal of clinical orthopaedics and trauma · 2024Article
- An explainable long short-term memory network for surgical site infection identification.Surgery · 2024Article
- Applications of natural language processing tools in the surgical journey.Frontiers in surgery · 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
- 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
- Data-Driven Technologies as Enablers for Value Creation in the Prevention of Surgical Site Infections: a Systematic Review.Journal of healthcare informatics research · 2023Review
- The development and validation of screening tools for semi-automated surveillance of surgical site infection following various surgeries.Frontiers in medicine · 2023Article
- Assessing the utility of deep neural networks in detecting superficial surgical site infections from free text electronic health record data.Frontiers in digital health · 2023Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesWe present the development and validation of a portable NLP approach for automated surveillance of SSIs. SUMMARY OF BACKGROUND DATA: The surveillance of SSIs is labor-intensive limiting the generalizability and scalability of surgical quality surveillance programs.
methodsWe abstracted patient clinical text notes after surgical procedures from 2 independent healthcare systems using different electronic healthcare records. An SSI detected as part of the American College of Surgeons' National Surgical Quality Improvement Program was used as the reference standard. We developed a rules-based NLP system (Easy Clinical Information Extractor [CIE]-SSI) for operative event-level detection of SSIs using an training cohort (4574 operative events) from 1 healthcare system and then conducted internal validation on a blind cohort from the same healthcare system (1850 operative events) and external validation on a blind cohort from the second healthcare system (15,360 operative events). EasyCIE-SSI performance was measured using sensitivity, specificity, and area under the receiver-operating-curve (AUC).
resultsThe prevalence of SSI was 4% and 5% in the internal and external validation corpora. In internal validation, EasyCIE-SSI had a sensitivity, specificity, AUC of 94%, 88%, 0.912 for the detection of SSI, respectively. In external validation, EasyCIE-SSI had sensitivity, specificity, AUC of 79%, 92%, 0.852 for the detection of SSI, respectively. The sensitivity of EasyCIE-SSI decreased in clean, skin/subcutaneous, and outpatient procedures in the external validation compared to internal validation.
conclusionAutomated surveillance of SSIs can be achieved using NLP of clinical notes with high sensitivity and specificity.
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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.