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

Autor:	Schulz, Marc-Andre, Thirion, Bertrand, Gramfort, Alexandre, Varoquaux, Gaël, Bzdok, Danilo
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Quantitative Biology - Neurons and Cognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at NIPS 2017 Workshop on Machine Learning for Health
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2110.06135 Zobrazit plný text záznamu View this record from Arxiv