Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction

• URL: http://arxiv.org/abs/2110.06135v1
• Date: Tue, 12 Oct 2021 16:25:50 GMT
• Title: Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction
• Authors: Marc-Andre Schulz, Bertrand Thirion, Alexandre Gramfort, Ga\"el Varoquaux, Danilo Bzdok
• Abstract summary: Low-dimensional embedding spaces can be derived from the UK Biobank population dataset to enhance data-scarce prediction of health indicators, lifestyle and demographic characteristics. Performances gains from semisupervison approaches will probably become an important ingredient for various medical data science applications.
• Score: 102.23901690661916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality data accumulation is now becoming ubiquitous in the health domain. There is increasing opportunity to exploit rich data from normal subjects to improve supervised estimators in specific diseases with notorious data scarcity. We demonstrate that low-dimensional embedding spaces can be derived from the UK Biobank population dataset and used to enhance data-scarce prediction of health indicators, lifestyle and demographic characteristics. Phenotype predictions facilitated by Variational Autoencoder manifolds typically scaled better with increasing unlabeled data than dimensionality reduction by PCA or Isomap. Performances gains from semisupervison approaches will probably become an important ingredient for various medical data science applications.

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