ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2017
Surveillance of Peripheral Arterial Disease Cases Using Natural Language Processing of Clinical Notes.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Artificial Intelligence of Arterial Doppler Waveforms to Predict Major Adverse Outcomes Among Patients Evaluated for Peripheral Artery Disease.Journal of the American Heart Association · 2024Trial
- A narrative review of imaging misallocation in peripheral artery disease: a value-based medicine perspective.Cardiovascular diagnosis and therapy · 2025Review
- Peripheral arterial disease prevalence among sepsis hospitalizations and associated outcomes.Journal of vascular surgery · 2025Article
- Building a best-in-class automated de-identification tool for electronic health records through ensemble learning.Patterns (New York, N.Y.) · 2021Article
- Adherence and exercise mode in supervised exercise therapy for peripheral artery disease.Journal of vascular nursing : official publication of the Society for Peripheral Vascular Nursing · 2020Article
- Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes.NPJ digital medicine · 2020Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Peripheral arterial disease (PAD) is a chronic disease that affects millions of people worldwide and yet remains underdiagnosed and undertreated. Early detection is important, because PAD is strongly associated with an increased risk of mortality and morbidity. In this study, we built a PAD surveillance system using natural language processing (NLP) for early detection of PAD from narrative clinical notes. Our NLP algorithm had excellent positive predictive value (0.93) and identified 41% of PAD cases before the initial ankle-brachial index (ABI) test date while in 12% of cases the NLP algorithm detected PAD on the same date as the ABI (the gold standard for comparison). Hence, our system ascertains PAD patients in a timely and accurate manner. In conclusion, our PAD surveillance NLP algorithm has the potential for translation to clinical practice for use in reminding clinicians to order ABI tests in patients with suspected PAD and to reinforce the implementation of guideline recommended risk modification strategies in patients diagnosed with PAD.
Identifiers
28815100PMC5543345W2786925566What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.