ArticleBJS open2024
Assessing the utility of natural language processing for detecting postoperative complications from free medical text.
Article in BJS open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- State-of-the-art review in AI and digital technologies for spine deformity.Spine deformity · 2026Review
- Transforming perioperative care: The current landscape and future trajectory of artificial intelligence in anesthesia-A narrative review.The Journal of international medical research · 2026Review
- A Clinical Decision Support System for Post-Surgical Cardiovascular Remote Monitoring.Clinics and practice · 2026Review
- Validation of natural language processing for surgical complication surveillance: detecting 11 postoperative complications from electronic health records.BMJ connections surgery · 2026Article
- 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
- The incremental value of unstructured data via natural language processing in machine learning-based COVID-19 mortality prediction: a comparative study.BMC medical informatics and decision making · 2025Article
- Applications of Natural Language Processing in Otolaryngology: A Scoping Review.The Laryngoscope · 2025Article
- Dataset of anonymized discharge summaries of sepsis patients from a Brazilian tertiary hospital for NLP applications.Data in brief · 2025Article
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Authors and funding
4 authors.
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Abstract
backgroundPostoperative complication rates are often assessed through administrative data, although this method has proven to be imprecise. Recently, new developments in natural language processing have shown promise in detecting specific phenotypes from free medical text. Using the clinical challenge of extracting four specific and frequently undercoded postoperative complications (pneumonia, urinary tract infection, sepsis, and septic shock), it was hypothesized that natural language processing would capture postoperative complications on a par with human-level curation from electronic health record free medical text.
methodsElectronic health record data were extracted for surgical cases (across 11 surgical sub-specialties) from 18 hospitals in the Capital and Zealand regions of Denmark that were performed between May 2016 and November 2021. The data set was split into training/validation/test sets (30.0%/48.0%/22.0%). Model performance was compared with administrative data and manual extraction of the test data set.
resultsData were obtained for 17 486 surgical cases. Natural language processing achieved a receiver operating characteristic area under the curve of 0.989 for urinary tract infection, 0.993 for pneumonia, 0.992 for sepsis, and 0.998 for septic shock, whereas administrative data achieved a receiver operating characteristic area under the curve of 0.595 for urinary tract infection, 0.624 for pneumonia, 0.571 for sepsis, and 0.625 for septic shock.
conclusionThe natural language processing approach was able to capture complications with acceptable performance, which was superior to administrative data. In addition, the model performance approached that of manual curation and thereby offers a potential pathway for complete real-time coverage of postoperative complications across surgical procedures based on natural language processing assessment of electronic health record free medical text.
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