ArticleThe Journal of arthroplasty2021
Use of Natural Language Processing Algorithms to Identify Common Data Elements in Operative Notes for Knee Arthroplasty.
Article in The Journal of arthroplasty, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses 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.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in total and unicompartmental knee arthroplasty.BMC musculoskeletal disorders · 2024Pooled it
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration.Bioengineering (Basel, Switzerland) · 2026Review
- Development and validation of natural language processing pipelines to extract injury and surgery-related data elements from ACL reconstruction operative reports.BMC musculoskeletal disorders · 2026Article
- A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 3: How orthopaedic research benefits from the implementation of artificial intelligence.Journal of experimental orthopaedics · 2025Review
- Balancing Ethics and Innovation: Can Artificial Intelligence Safely Transform Emergency Surgery? A Narrative Perspective.Journal of clinical medicine · 2025Article
- Artificial intelligence in medical and biological research: promise and perils of ChatGPT and DeepSeek in advancing healthcare.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
- Deep learning classification of pediatric spinal radiographs for use in large scale imaging registries.Spine deformity · 2024Article
- Overview of Artificial Intelligence Research Within Hip and Knee Arthroplasty.Arthroplasty today · 2024Article
- Prevalence and Predictors of Concomitant Meniscal Surgery During Pediatric and Adolescent ACL Reconstruction: Analysis of 4729 Patients Over 20 Years at a Tertiary-Care Regional Children's Hospital.Orthopaedic journal of sports medicine · 2024Article
- Global mapping of institutional and hospital-based (Level II-IV) arthroplasty registries: a scoping review.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2024Article
- Applications of natural language processing tools in the surgical journey.Frontiers in surgery · 2024Review
- Systematic evaluation of common natural language processing techniques to codify clinical notes.PloS one · 2024Article
- External Validation of Natural Language Processing Algorithms to Extract Common Data Elements in THA Operative Notes.The Journal of arthroplasty · 2023Article
- The IMPACT framework and implementation for accessible in silico clinical phenotyping in the digital era.NPJ digital medicine · 2023Review
- Natural language processing: using artificial intelligence to understand human language in orthopedics.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2023Review
- Utilization of Natural Language Processing Software to Identify Worrisome Pancreatic Lesions.Annals of surgical oncology · 2022Article
- The development and deployment of machine learning models.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2022Article
- Artificial intelligence in knee arthroplasty: current concept of the available clinical applications.Arthroplasty (London, England) · 2022Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundNatural language processing (NLP) methods have the capability to process clinical free text in electronic health records, decreasing the need for costly manual chart review, and improving data quality. We developed rule-based NLP algorithms to automatically extract surgery specific data elements from knee arthroplasty operative notes.
methodsWithin a cohort of 20,000 knee arthroplasty operative notes from 2000 to 2017 at a large tertiary institution, we randomly selected independent pairs of training and test sets to develop and evaluate NLP algorithms to detect five major data elements. The size of the training and test datasets were similar and ranged between 420 to 1592 surgeries. Expert rules using keywords in operative notes were used to implement NLP algorithms capturing: (1) category of surgery (total knee arthroplasty, unicompartmental knee arthroplasty, patellofemoral arthroplasty), (2) laterality of surgery, (3) constraint type, (4) presence of patellar resurfacing, and (5) implant model (catalog numbers). We used institutional registry data as our gold standard to evaluate the NLP algorithms.
resultsNLP algorithms to detect the category of surgery, laterality, constraint, and patellar resurfacing achieved 98.3%, 99.5%, 99.2%, and 99.4% accuracy on test datasets, respectively. The implant model algorithm achieved an F1-score (harmonic mean of precision and recall) of 99.9%.
conclusionsNLP algorithms are a promising alternative to costly manual chart review to automate the extraction of embedded information within knee arthroplasty operative notes. Further validation in other hospital settings will enhance widespread implementation and efficiency in data capture for research and clinical purposes. LEVEL OF EVIDENCE: Level III.
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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.