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Venous Thrombosis III: Interprofessional Care01:29

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Venous thrombosis requires effective prevention and treatment strategies to improve patient outcomes and reduce potential complications.Prevention StrategiesHealthcare providers must prioritize preventing venous thromboembolism (VTE) for all adult patients upon admission. Interventions depend on bleeding and thrombosis risk, medical history, current medications, diagnoses, planned procedures, and patient preferences. Patients on bed rest should change positions every two hours and, if not...
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Venous thrombosis, the most common disorder of the veins, involves the formation of a thrombus or blood clot associated with vein inflammation. It can be classified as either superficial vein thrombosis or deep vein thrombosis.Superficial Vein Thrombosis: This involves the formation of a thrombus in a superficial vein, usually the greater or lesser saphenous vein. Though less severe than deep vein thrombosis (DVT), SVT can lead to complications if untreated.Deep Vein Thrombosis (DVT): This...
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The key difference between Superficial Vein Thrombosis (SVT) and Deep Vein Thrombosis (DVT) lies in their location and severity.Clinical ManifestationsSVT typically presents with localized pain, tenderness, and redness along the course of a superficial vein, often accompanied by a palpable, cord-like structure under the skin. This condition is usually less dangerous than DVT but can be uncomfortable and may lead to complications such as cellulitis or, rarely, a clot extension into the deep...
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Nursing management begins with a thorough assessment of the patient's health history. Key factors include trauma to veins, peripherally inserted central catheters, varicose veins, recent pregnancy or childbirth, surgery, bacteremia, prolonged bed rest, atrial fibrillation, COPD, heart failure, cancer, coagulation disorders, myocardial infarction, spinal cord injury, stroke, prolonged travel, recent bone fractures, and dehydration. Review medication intake, particularly oral contraceptives,...
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Development and validation of venous thromboembolism-bidirectional encoder representations from transformers

Omid Jafari1, Shengling Ma1, Barbara D Lam2

  • 1Section of Hematology-Oncology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.

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Summary

A new natural language processing (NLP) model, VTE-BERT, accurately detects venous thromboembolism (VTE) events longitudinally in cancer patients. This tool enhances VTE detection for large-scale thrombosis research.

Keywords:
artificial intelligencelarge language modelnatural language processingneoplasmsthromboembolism

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Clinical Research

Background:

  • Accurate and timely phenotyping of venous thromboembolism (VTE) is crucial for longitudinal studies.
  • A validated natural language processing (NLP) tool for VTE detection in real-world patient populations is currently lacking.

Purpose of the Study:

  • To develop and externally validate a novel NLP platform, NLPMed, to facilitate thrombosis research.
  • To create and validate VTE-BERT, a specialized NLP model for detecting acute VTE events and their anatomical locations longitudinally.

Main Methods:

  • A novel NLP platform, NLPMed, was developed for data preprocessing, annotation, and model finetuning.
  • Bio_ClinicalBERT was finetuned using clinical notes from patients with cancer to create VTE-BERT.
  • The VTE-BERT model underwent internal and external validation in patient cohorts from two healthcare institutions.

Main Results:

  • VTE-BERT achieved high performance during training, reaching 95% precision and 98% recall.
  • Internal validation demonstrated 95% precision and 91% recall.
  • External validation in an independent dataset yielded 85% precision and 92% recall.

Conclusions:

  • An efficient NLP model, VTE-BERT, was successfully trained and externally validated for longitudinal VTE event detection.
  • The adoption of VTE-BERT is expected to accelerate thrombosis research by improving large-scale VTE detection.
  • This NLP tool can reduce the time and cost associated with manual chart review in big data epidemiological studies.