Artificial Intelligence in Venous Thromboembolism Prevention: A Narrative Review of Machine Learning, Deep Learning,...

Area of Science:

• Medical Informatics
• Cardiovascular Medicine
• Artificial Intelligence in Healthcare

Background:

• Venous thromboembolism (VTE), encompassing deep vein thrombosis and pulmonary embolism, is a major global health concern with significant preventable morbidity and mortality.
• Current methods for identifying and managing high-risk VTE patients, including clinical prediction models and imaging, have limitations.
• Artificial intelligence (AI) presents a transformative opportunity to advance VTE prevention strategies.

Purpose of the Study:

• To review and synthesize current evidence on the application of AI technologies in VTE prevention.
• To explore the capabilities of machine learning (ML), deep learning (DL), and natural language processing (NLP) in enhancing VTE risk assessment and management.
• To discuss the challenges and requirements for integrating AI into clinical practice for VTE prevention.

Main Methods:

• Narrative review of existing literature on AI applications in VTE prevention.
• Analysis of supervised ML algorithms (e.g., random forests, SVMs, gradient boosting) for predictive modeling using electronic health record (EHR) data.
• Examination of DL models (e.g., CNNs) for medical image analysis and NLP for extracting clinical information from unstructured notes.
• Consideration of emerging AI techniques, including wearable device data and time-series analysis.

Main Results:

• Supervised ML algorithms demonstrate improved predictive performance over traditional models by capturing complex patterns in EHR data.
• DL models achieve diagnostic accuracy in interpreting imaging data comparable to expert radiologists.
• NLP effectively extracts crucial risk information from unstructured clinical notes, and AI integration with wearable data enables dynamic risk assessment.

Conclusions:

• AI technologies, including ML, DL, and NLP, offer significant potential to improve the accuracy and efficiency of VTE risk identification and management.
• Successful clinical integration necessitates prospective validation, inter-institutional collaboration, and seamless implementation into clinical decision support systems.
• AI-driven approaches promise to enhance VTE prevention, ultimately reducing patient morbidity and mortality.