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Related Concept Videos

Venous Thrombosis III: Interprofessional Care01:29

Venous Thrombosis III: Interprofessional Care

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...
Venous Thrombosis II: Clinical Manifestations and Diagnostic Studies01:20

Venous Thrombosis II: Clinical Manifestations and Diagnostic Studies

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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Related Experiment Video

Updated: Jul 12, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Automated risk scoring for venous thromboembolism using large language models with expert knowledge-augmented

Jing Ma1, Dingyi Wang2, Yaqian Zhang3

  • 1Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.

NPJ Digital Medicine
|July 9, 2026
PubMed
Summary

Large language models (LLMs) with expert knowledge-augmented prompts can accurately automate venous thromboembolism (VTE) risk scoring using electronic health records (EHRs). This supports efficient thrombosis prevention in hospitalized patients.

Related Experiment Videos

Last Updated: Jul 12, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

  • Medical Informatics
  • Clinical Decision Support Systems
  • Artificial Intelligence in Healthcare

Background:

  • Venous thromboembolism (VTE) is a significant, preventable complication in hospitalized patients.
  • Standardized VTE risk scoring using unstructured electronic health records (EHRs) is challenging.
  • Current risk assessment tools require manual data extraction and interpretation.

Purpose of the Study:

  • To evaluate the efficacy of large language models (LLMs) in automating VTE risk scoring.
  • To compare expert knowledge-augmented prompts with basic and complex prompts for Padua and Caprini scores.
  • To assess the performance of LLMs in risk stratification for thrombosis prevention.

Main Methods:

  • Retrospective analysis of anonymized EHRs from a nationwide multicenter cohort (30 hospitals).
  • Development of expert knowledge-augmented prompts for Padua and Caprini scores via a Delphi process.
  • Evaluation of six open-source LLMs using prevalence-adjusted bias-adjusted kappa (PABAK) and F1 scores.

Main Results:

  • Expert knowledge-augmented prompts achieved the highest performance for both Padua and Caprini scores.
  • In the test dataset, mean item-level PABAK and F1 scores exceeded 0.90 for most items.
  • Padua scoring demonstrated superior risk stratification (PABAK 0.92, F1 0.96) compared to Caprini (PABAK 0.73, F1 0.64).

Conclusions:

  • LLMs with expert knowledge-augmented prompting can efficiently automate Padua and Caprini scoring.
  • This approach supports automated risk stratification, enhancing thrombosis prevention workflows.
  • The findings suggest a promising role for AI in improving VTE prophylaxis in clinical practice.