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Related Concept Videos

Venous Thrombosis III: Interprofessional Care01:29

Venous Thrombosis III: Interprofessional Care

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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 II: Clinical Manifestations and Diagnostic Studies01:20

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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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Venous Thrombosis I: Introduction01:30

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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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Venous Thrombosis IV: Nursing Management01:30

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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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Related Experiment Video

Updated: Jan 14, 2026

A Multicenter MRI Protocol for the Evaluation and Quantification of Deep Vein Thrombosis
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Predicting deep vein thrombosis using machine learning and blood routine analysis.

Jie Su1,2, Yuechao Tang3, Yanan Wang4

  • 1Medical Neurobiology Laboratory, Inner Mongolia Medical University, Hohhot, China.

Frontiers in Big Data
|October 22, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models using routine blood tests can effectively predict deep vein thrombosis (DVT) risk. This approach aids in early DVT diagnosis, improving patient outcomes without added medical burden.

Keywords:
SHAP analysisblood routinedeep vein thrombosismachine learningprediction model

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Area of Science:

  • Medical Diagnostics
  • Machine Learning in Healthcare
  • Hematology

Background:

  • Deep vein thrombosis (DVT) is a significant health concern with severe complications like pulmonary embolism and potential limb amputation.
  • Early diagnosis of DVT is crucial to prevent life-threatening outcomes and long-term disability.

Purpose of the Study:

  • To develop and evaluate machine learning models for early deep vein thrombosis (DVT) diagnosis using routine blood analysis.
  • To identify key blood indices predictive of DVT risk through advanced feature selection techniques.

Main Methods:

  • Retrospective analysis of 658 DVT patients and 1,418 healthy controls (January 2022 - June 2023).
  • SHAP (SHapley Additive exPlanations) analysis for identifying significant blood routine indices.
  • Construction and evaluation of six machine learning models (kNN, LR, DT, RF, SVM, ANN) using selected features, assessed by AUC.

Main Results:

  • SHAP analysis identified ten key blood routine indices for DVT prediction.
  • All six machine learning models demonstrated strong predictive performance (AUC > 0.8, accuracy > 70%, sensitivity/specificity > 70%).
  • The Random Forest (RF) model showed the highest performance in assessing DVT risk.

Conclusions:

  • Machine learning models utilizing routine blood tests offer a promising avenue for early DVT risk prediction.
  • These models can facilitate timely diagnosis and intervention, potentially reducing patient morbidity and mortality.
  • Further validation and refinement are recommended to enhance clinical applicability and integration into routine practice.