Machine-Learning Diagnosis
Cross-source consensus on Machine-Learning Diagnosis from 1 sources and 4 claims.
1 sources · 4 claims
Uses
Risks & contraindications
Comparisons
Highlighted claims
- The first modelling objective is to estimate the pretest probability that an angina patient has ANOCA rather than obstructive CAD. — Development and cross-site validation of machine-learning models for diagnosis and prognosis of stable angina with and without obstructive coronary artery disease: a study protocol
- The diagnostic probability is intended to guide decisions about angiography, non-invasive testing, and coronary function testing. — Development and cross-site validation of machine-learning models for diagnosis and prognosis of stable angina with and without obstructive coronary artery disease: a study protocol
- Traditional obstructive-CAD risk tools can label ANOCA patients low-risk and reduce access to further testing or intensive therapy. — Development and cross-site validation of machine-learning models for diagnosis and prognosis of stable angina with and without obstructive coronary artery disease: a study protocol
- Traditional stress testing may miss occult coronary abnormalities in ANOCA. — Development and cross-site validation of machine-learning models for diagnosis and prognosis of stable angina with and without obstructive coronary artery disease: a study protocol