Use of Deep Learning to Identify Peripheral Arterial Disease Cases From Narrative Clinical Notes.
Journal:
The Journal of surgical research
PMID:
39454287
Abstract
INTRODUCTION: Peripheral arterial disease (PAD) is the leading cause of amputation in the United States. Despite affecting 8.5 million Americans and more than 200 million people globally, there are significant gaps in awareness by both patients and providers. Ongoing efforts to raise PAD awareness among both the public and health-care professionals have not met widespread success. Thus, there is a need for alternative methods for identifying PAD patients. One potentially promising strategy leverages natural language processing (NLP) to digitally screen patients for PAD. Prior approaches have applied keyword search (KWS) to billing codes or unstructured clinical narratives to identify patients with PAD. However, KWS is limited by its lack of flexibility, the need for manual algorithm development, inconsistent validation, and an inherent failure to capture patients with undiagnosed PAD. Recent advances in deep learning (DL) allow modern NLP models to learn a conceptual representation of the verbiage associated with PAD. This capability may overcome the characteristic constraints of applying strict rule-based algorithms (i.e., searching for a disease-defining set of keywords or billing codes) to real-world clinical data. Herein, we investigate the use of DL to identify patients with PAD from unstructured notes in the electronic health record (EHR).