Tailored risk assessment and forecasting in intermittent claudication.

Bharadhwaj RavindhranJonathon ProsserArthur J M LimBhupesh MishraRoss Lathan Louise H HitchmanGeorge E SmithDaniel CarradiceIan C ChetterDhaval ThakkerSean Pymer

Published in: BJS open (2024)

The machine-learning algorithm successfully predicts outcomes for patients with intermittent claudication across various initial treatment strategies, offering potential for improved risk stratification and patient outcomes.

Keyphrases

machine learning
risk assessment
human health
high intensity
peripheral artery disease
artificial intelligence
deep learning
big data
heavy metals
smoking cessation
type diabetes
metabolic syndrome
neural network
insulin resistance