Annie Gu

TL;DR: This work addresses issues by providing a standard, multi-institutional database and a novel scoring metric through a public competition: the PhysioNet/Computing in Cardiology Challenge 2020, setting a new bar in reproducibility for public data science competitions.

TL;DR: A cost-based evaluation metric is devised that captures the costs of screening, treatment, and diagnostic errors, allowing us to investigate the benefits of algorithmic pre-screening and facilitate the development of more clinically relevant algorithms.

TL;DR: This Challenge provided several innovations, including a novel evaluation metric that considers different misclassification errors for different cardiac abnormalities, reflecting the clinical reality that some diagnoses have similar outcomes and varying risks.

TL;DR: The submission of working, open-source code for both training and testing and the use of a novel evaluation metric improved the reproducibility, generalizability, and applicability of the research conducted during the 2021 PhysioNet Challenge.

TL;DR: The PhysioNet/Computing in Cardiology Challenge 2020 as mentioned in this paper focused on the identification of cardiac abnormalities in 12-lead electrocardiogram (ECG) recordings, which encouraged the development of generalizable, reproducible, and clinically relevant algorithms.