Learning disease relationships from clinical drug trials.
Journal:
Journal of the American Medical Informatics Association : JAMIA
PMID:
27189012
Abstract
OBJECTIVE: Our objective is to test the limits of the assumption that better learning from data in medicine requires more granular data. We hypothesize that clinical trial metadata contains latent scientific, clinical, and regulatory expert knowledge that can be accessed to draw conclusions about the underlying biology of diseases. We seek to demonstrate that this latent information can be uncovered from the whole body of clinical trials.