An interpretable, clinically-aligned AI paradigm for VTE risk prediction: an approach using LLMs and compound attention
Abstract
Manual VTE risk assessment is often inconsistent, while many high-performing AI models remain difficult to audit clinically. We propose an interpretable and clinically aligned paradigm that uses locally deployed LLMs to structure EHR narratives and an attention-based model (EMAX) to predict expert-adjudicated Caprini RAM (2010) risk. From 113,836 raw encounter records, we curated 14,808 eligible records with documented expert Caprini scores for supervised learning. On the test set, EMAX achieved an AUC of 0.9513. The framework provides an end-to-end pathway for accurate and adoptable VTE risk stratification.
Fields of Study & Subjects
Venous Thromboembolism Diagnosis and Management
Diagnosis and Treatment of Venous Diseases
Atrial Fibrillation Management and Outcomes
Publication details
- Journal / source
- Computer Methods in Biomechanics & Biomedical Engineering
- Publisher
- Informa UK Limited
- Pages
- 1–13
- Publication date
- 2 July 2026
- Language
- EN