# Artificial Intelligence in Venous Thromboembolism Prevention: A Narrative Review of Machine Learning, Deep Learning, and Natural Language Processing

## TL;DR

This paper reviews how artificial intelligence, including machine learning and deep learning, can improve venous thromboembolism prevention by analyzing health data and clinical notes.

## Contribution

The paper provides a comprehensive narrative review of AI applications in venous thromboembolism prevention, emphasizing novel integration of machine learning and natural language processing.

## Key findings

- Supervised machine learning algorithms improve predictive performance by capturing complex relationships in electronic health records.
- Deep learning models, such as convolutional neural networks, achieve diagnostic accuracy comparable to expert radiologists in interpreting imaging data.
- Natural language processing extracts risk-relevant information from unstructured clinical notes, enhancing risk assessment.

## Abstract

Venous thromboembolism (VTE), which includes deep vein thrombosis and pulmonary embolism, is a significant and preventable cause of morbidity and mortality worldwide. Despite the existence of clinical prediction models, biomarker-based risk assessments, and imaging techniques, gaps remain in accurately identifying and managing high-risk patients. In recent years, artificial intelligence has emerged as a transformative tool in healthcare, offering promising applications for enhancing VTE prevention strategies. This narrative review synthesizes current evidence on the use of artificial intelligence (AI) technologies including machine learning (ML), deep learning (DL), and natural language processing (NLP). We explore how supervised ML algorithms, such as random forests, support vector machines, and gradient boosting, improve predictive performance compared to traditional models by capturing complex, nonlinear relationships within electronic health record data. We also examine the role of DL models, particularly convolutional neural networks, in interpreting imaging data, achieving diagnostic accuracies comparable to expert radiologists. Additionally, the review highlights NLP applications in extracting risk-relevant information from unstructured clinical notes and the emerging integration of wearable device data and time-series analysis for dynamic risk assessment. We argue that the successful integration of AI into routine VTE prevention workflows requires rigorous prospective validation, cross-institutional collaboration, and thoughtful implementation into clinical decision support systems.