ArticleSurgery2021
Natural language processing for the surveillance of postoperative venous thromboembolism.
Article in Surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- 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
- An Extraction Tool for Venous Thromboembolism Symptom Identification in Primary Care Notes to Facilitate Electronic Clinical Quality Measure Reporting: Algorithm Development and Validation Study.JMIR medical informatics · 2025Article
- Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment.The journal of allergy and clinical immunology. Global · 2024Article
- Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot.Bleeding, thrombosis and vascular biology · 2024Article
- ClotCatcher: a novel natural language model to accurately adjudicate venous thromboembolism from radiology reports.BMC medical informatics and decision making · 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
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Abstract
backgroundThe objective of this study was to develop a portal natural language processing approach to aid in the identification of postoperative venous thromboembolism events from free-text clinical notes.
methodsWe abstracted clinical notes from 25,494 operative events from 2 independent health care systems. A venous thromboembolism detected as part of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) was used as the reference standard. A natural language processing engine, easy clinical information extractor-pulmonary embolism/deep vein thrombosis (EasyCIE-PEDVT), was trained to detect pulmonary embolism and deep vein thrombosis from clinical notes. International Classification of Diseases (ICD) discharge diagnosis codes for venous thromboembolism were used as baseline comparators. The classification performance of EasyCIE-PEDVT was compared with International Classification of Diseases codes using sensitivity, specificity, area under the receiver operating characteristic curve, using an internal and external validation cohort.
resultsTo detect pulmonary embolism, EasyCIE-PEDVT had a sensitivity of 0.714 and 0.815 in internal and external validation, respectively. To detect deep vein thrombosis, EasyCIE-PEDVT had a sensitivity of 0.846 and 0.849 in internal and external validation, respectively. EasyCIE-PEDVT had significantly higher discrimination for deep vein thrombosis compared with International Classification of Diseases codes in internal validation (area under the receiver operating characteristic curve: 0.920 vs 0.761; P < .001) and external validation (area under the receiver operating characteristic curve: 0.921 vs 0.794; P < .001). There was no significant difference in the discrimination for pulmonary embolism between EasyCIE-PEDVT and ICD codes.
conclusionAccurate surveillance of postoperative venous thromboembolism may be achieved using natural language processing on clinical notes in 2 independent health care systems. These findings suggest natural language processing may augment manual chart abstraction for large registries such as NSQIP.
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