ArticleJournal of vascular surgery2016
The use of machine learning for the identification of peripheral artery disease and future mortality risk.
Article in Journal of vascular surgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 2 of them syntheses that pooled it.
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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
60 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Global performance of machine learning models to predict all-cause mortality: systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Diagnosis models to predict peripheral arterial disease: a systematic review and meta analysis.Scientific reports · 2025Pooled it
- 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
- Performance and Fairness of a Peripheral Artery Disease Detection Model Across Patient Phenotypes and Health Systems.medRxiv : the preprint server for health sciences · 2026Article
- Vascular Precision Medicine: A Ray of Sunshine for the Therapies of the Present and Future.Angiology · 2026Article
- [Artificial intelligence in surgical disciplines: Clinical application, advantages, and potential-a Delphi expert consensus].Urologie (Heidelberg, Germany) · 2026Article
- Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.Circulation. Population health and outcomes · 2026Review
- Deep-learning-based diagnostic models for transcutaneous oxygen pressure-defined peripheral arterial disease and lower extremity wound healing in patients with diabetic foot.BMC medical informatics and decision making · 2026Article
- An explainable machine learning model for type B aortic dissection identification: development and internal evaluation.Frontiers in public health · 2026Article
- Construction of diagnostic model and subtype analysis of major depressive disorder based on PANoptosis key genes.BMC psychiatry · 2025Article
- A Machine Learning Tool to Predict Survival After First Surgery in Peripheral Artery Disease Patients.Journal of cardiovascular translational research · 2025Article
- Monitoring strategies for continuous evaluation of deployed clinical prediction models.Journal of biomedical informatics · 2025Article
- Article
- Current Applications and Future Perspectives of Artificial and Biomimetic Intelligence in Vascular Surgery and Peripheral Artery Disease.Biomimetics (Basel, Switzerland) · 2024Review
- Contrast-Enhanced Magnetic Resonance Imaging Based T1 Mapping and Extracellular Volume Fractions Are Associated with Peripheral Artery Disease.Journal of cardiovascular development and disease · 2024Article
- CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology.Cancers · 2024Article
- A machine learning-based approach to identify peripheral artery disease using texture features from contrast-enhanced magnetic resonance imaging.Magnetic resonance imaging · 2024Article
- Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation.Scientific reports · 2024Article
- Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review.Applied bionics and biomechanics · 2024Article
- Identifying the risk factors of ICU-acquired fungal infections: clinical evidence from using machine learning.Frontiers in medicine · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveA key aspect of the precision medicine effort is the development of informatics tools that can analyze and interpret "big data" sets in an automated and adaptive fashion while providing accurate and actionable clinical information. The aims of this study were to develop machine learning algorithms for the identification of disease and the prognostication of mortality risk and to determine whether such models perform better than classical statistical analyses.
methodsFocusing on peripheral artery disease (PAD), patient data were derived from a prospective, observational study of 1755 patients who presented for elective coronary angiography. We employed multiple supervised machine learning algorithms and used diverse clinical, demographic, imaging, and genomic information in a hypothesis-free manner to build models that could identify patients with PAD and predict future mortality. Comparison was made to standard stepwise linear regression models.
resultsOur machine-learned models outperformed stepwise logistic regression models both for the identification of patients with PAD (area under the curve, 0.87 vs 0.76, respectively; P = .03) and for the prediction of future mortality (area under the curve, 0.76 vs 0.65, respectively; P = .10). Both machine-learned models were markedly better calibrated than the stepwise logistic regression models, thus providing more accurate disease and mortality risk estimates.
conclusionsMachine learning approaches can produce more accurate disease classification and prediction models. These tools may prove clinically useful for the automated identification of patients with highly morbid diseases for which aggressive risk factor management can improve outcomes.
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