ArticleSurgery2018
Autonomous detection, grading, and reporting of postoperative complications using natural language processing.
Article in Surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis.Blood advances · 2024Pooled it
- Validation of a Semiautomated Natural Language Processing-Based Procedure for Meta-Analysis of Cancer Susceptibility Gene Penetrance.JCO clinical cancer informatics · 2019Pooled it
- Prediction and Diagnosis of Venous Thromboembolism Using Artificial Intelligence Approaches: A Systematic Review and Meta-Analysis.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisPooled it
- Utilization and prospects of artificial intelligence in the diagnosis, prediction, and treatment of erectile dysfunction.Sexual medicine · 2026Review
- Automated natural language processing to identify venous thromboembolism from diagnostic imaging reports.Blood vessels, thrombosis & hemostasis · 2026Article
- Impact of aspirin therapy on progression of thoracic and abdominal aortic aneurysms.Atherosclerosis · 2025Article
- Language Models for Multilabel Document Classification of Surgical Concepts in Exploratory Laparotomy Operative Notes: Algorithm Development Study.JMIR medical informatics · 2025Article
- Artificial Intelligence-Based Clinical Decision-Making in Erectile Dysfunction: a Narrative Review.Current urology reports · 2024Review
- Developing Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study.Thrombosis and haemostasis · 2023Article
- Natural Language Processing in a Clinical Decision Support System for the Identification of Venous Thromboembolism: Algorithm Development and Validation.Journal of medical Internet research · 2023Article
- Classification of neurologic outcomes from medical notes using natural language processing.Expert systems with applications · 2023Article
- Applications of artificial intelligence in the diagnosis and prediction of erectile dysfunction: a narrative review.International journal of impotence research · 2023Review
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- Natural Language Processing: from Bedside to Everywhere.Yearbook of medical informatics · 2022Article
- Racial differences in venous thromboembolism: A surveillance program in Durham County, North Carolina.Research and practice in thrombosis and haemostasis · 2022Article
- Natural language processing for the surveillance of postoperative venous thromboembolism.Surgery · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
introductionNatural language processing, a computer science technique that allows interpretation of narrative text, is infrequently used to identify surgical complications. We designed a natural language processing algorithm to identify and grade the severity of deep venous thrombosis and pulmonary embolism (together: venous thromboembolism).
methodsPatients from our 2011-2014 American College of Surgeons National Surgical Quality Improvement Project cohorts with a duplex ultrasound or a computerized tomography angiography of the chest performed within 30 days of surgery were divided into training and validation datasets. A "bag of words" approach classified the reports; other electronic health record data classified the venous thromboembolism's severity.
resultsOf the 10,295 American College of Surgeons National Surgical Quality Improvement Project patients, 251 were used in our deep venous thromboses validation cohort (273 total ultrasounds) and 506 in our pulmonary embolisms cohort (552 total computerized tomography angiographies). For deep venous thromboses the sensitivity and specificity were 85.1% and 94.6%, while for pulmonary embolisms they were 90% and 98.7%. Most discordances were due to lack of imaging documentation of a deep venous thrombosis (28/41, 68.3%) or pulmonary embolism (6/6, 100%). Most deep venous thromboses (28 patients, 54.6%) and pulmonary embolisms (25 patients, 75.8%) required administration of therapeutic intravenous or subcutaneous anticoagulation.
conclusionNatural language processing can reliably detect the presence of postoperative venous thromboembolisms, and its use should be expanded for the detection of other conditions from narrative documentation.
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