ClotCatcher: a novel natural language model to accurately adjudicate venous thromboembolism from radiology reports.

ABSTRACT:

Introduction

Accurate identification of venous thromboembolism (VTE) is critical to develop replicable epidemiological studies and rigorous predictions models. Traditionally, VTE studies have relied on international classification of diseases (ICD) codes which are inaccurate - leading to misclassification bias. Here, we developed ClotCatcher, a novel deep learning model that uses natural language processing to detect VTE from radiology reports.

Methods

Radiology reports to detect VTE were obtained from patients admitted to Emory University Hospital (EUH) and Grady Memorial Hospital (GMH). Data augmentation was performed using the Google PEGASUS paraphraser. This data was then used to fine-tune ClotCatcher, a novel deep learning model. ClotCatcher was validated on both the EU

SUBMITTER: Wang J 

PROVIDER: S-EPMC10652606 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature