ArticleCirculation. Cardiovascular interventions2020
Use of Natural Language Processing to Improve Identification of Patients With Peripheral Artery Disease.
Article in Circulation. Cardiovascular interventions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
- From Bits to Bedside: From Data to Knowledge in Vascular Surgery.Journal of clinical medicine · 2026Review
- Cumulative Metabolic Exposure to Hyperglycemia and Risk of Cardiovascular and Limb Events in Peripheral Artery Disease.medRxiv : the preprint server for health sciences · 2026Article
- Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.Circulation. Population health and outcomes · 2026Review
- Improving Retrieval Augmented Generation for Health Care by Fine-Tuning Clinical Embedding Models: Development and Evaluation Study.Journal of medical Internet research · 2026Article
- Natural Language Processing framework for identifying abdominal aortic aneurysm repairs using unstructured electronic health records.Scientific reports · 2025Article
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- Long-Term Outcomes of Peripheral Artery Disease in Veterans: Analysis of the Peripheral Artery Disease Long-Term Survival Study (PEARLS).Journal of the American Heart Association · 2025Article
- Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal.Health information management : journal of the Health Information Management Association of Australia · 2024Article
- Long-term Outcomes of Peripheral Artery Disease In Veterans: Analysis of the PEripheral ARtery Disease Long-term Survival Study (PEARLS).medRxiv : the preprint server for health sciences · 2024Article
- Current Applications and Future Perspectives of Artificial and Biomimetic Intelligence in Vascular Surgery and Peripheral Artery Disease.Biomimetics (Basel, Switzerland) · 2024Review
- Applications of natural language processing tools in the surgical journey.Frontiers in surgery · 2024Review
- Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review.Applied bionics and biomechanics · 2024Article
- Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder Qualitative Study.JMIR formative research · 2023Article
- A Glimpse Into the Black Box: Using Machine Learning to Prioritize Predictors of Vascular Disease.JACC. Advances · 2023Article
- Comprehensive Review of Natural Language Processing (NLP) in Vascular Surgery.EJVES vascular forum · 2023Review
- Ankle- and Toe-Brachial Index for Peripheral Artery Disease Identification: Unlocking Clinical Data Through Novel Methods.Circulation. Cardiovascular interventions · 2022Article
- Machine learning in vascular surgery: a systematic review and critical appraisal.NPJ digital medicine · 2022Review
- Translatability Analysis of National Institutes of Health-Funded Biomedical Research That Applies Artificial Intelligence.JAMA network open · 2022Article
- Artificial intelligence in atherosclerotic disease: Applications and trends.Frontiers in cardiovascular medicine · 2022Review
- Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes.Circulation research · 2021Review
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Abstract
backgroundPeripheral artery disease (PAD) is underrecognized, undertreated, and understudied: each of these endeavors requires efficient and accurate identification of patients with PAD. Currently, PAD patient identification relies on diagnosis/procedure codes or lists of patients diagnosed or treated by specific providers in specific locations and ways. The goal of this research was to leverage natural language processing to more accurately identify patients with PAD in an electronic health record system compared with a structured data-based approach.
methodsThe clinical notes from a cohort of 6861 patients in our health system whose PAD status had previously been adjudicated were used to train, test, and validate a natural language processing model using 10-fold cross-validation. The performance of this model was described using the area under the receiver operating characteristic and average precision curves; its performance was quantitatively compared with an administrative data-based least absolute shrinkage and selection operator (LASSO) approach using the DeLong test.
resultsThe median (SD) of the area under the receiver operating characteristic curve for the natural language processing model was 0.888 (0.009) versus 0.801 (0.017) for the LASSO-based approach alone (DeLong
conclusionsUsing a natural language processing approach in addition to partial cohort preprocessing with a LASSO-based model, we were able to meaningfully improve our ability to identify patients with PAD compared with an approach using structured data alone. This model has potential applications to both interventions targeted at improving patient care as well as efficient, large-scale PAD research. Graphic Abstract: A graphic abstract is available for this article.
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