Bridging the gap between machine learning and clinical applications
DOI:
https://doi.org/10.24170/23-3-8500Abstract
We previously discussed modern machine learning (ML) approaches as an alternative to traditional statistical tests in biological, clinical, and epidemiological research, with a focus on cardiac event prediction, followed by a workflow description for matching methods to data and clinical questions. Here, we highlight some successful clinical applications of ML pipelines, with reference to the tools previously summarised.
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