Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data.

Abstract excerpt

Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels), and where to look for them (by specifying the input variables). While appropriate for...

Topics

Share this publication in a Topic to start or enrich a Post.

• Share to Learning
• Share to Phenotype