Population scale latent space cohort matching for the improved use and exploration of observational trial data

Population scale latent space cohort matching for the improved use and exploration of observational trial data

A significant amount of clinical research is observational by nature and derived from medical records, clinical trials, and large-scale registries. While there is no substitute for randomized, controlled experimentation, such experiments or trials are often costly, time consuming, and even ethically...

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Bibliographic Details
Main Authors: Rachel Gologorsky, Sulaiman S. Somani, Sean N. Neifert, Aly A. Valliani, Katherine E. Link, Viola J. Chen, Anthony B. Costa, Eric K. Oermann
Format: Article
Language:English
Published: AIMS Press 2022-05-01
Series:Mathematical Biosciences and Engineering
Subjects:
artificial intelligence
autoencoders
cohort matching
data visualization
deep learning
manifold learning
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2022320?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/mbe.2022320?viewType=HTML

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