Predict clinical outcome
3 papers with code • 1 benchmarks • 1 datasets
A cost-based metric that considers the costs of algorithmic prescreening, expert screening, treatment, and diagnostic errors that result in late or missed treatments. This metric is further described here: https://moody-challenge.physionet.org/2022/
Benchmarks
These leaderboards are used to track progress in Predict clinical outcome
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CirCor DigiScope
Most implemented papers
Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022
Objective Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs for follow-up diagnostic screening and treatment, especially in resource-constrained environments.
Phonocardiogram Classification Using 1-Dimensional Inception Time Convolutional Neural Networks
Bsingstad/Heart-murmur-detection-2022-Simulab
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Computing in Cardiology 2022
These sounds are detected by auscultating the heart using a stethoscope, or more recently by a phonocardiogram (PCG).
Multi-modal Graph Learning over UMLS Knowledge Graphs
ratschlab/mmugl
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The results demonstrate the significance of multi-modal medical concept representations based on prior medical knowledge.
