Feature extraction and dimension reduction for networks is critical in a wide variety of domains. Efficiently and accurately learning features for multiple graphs has important applications in statistical inference on graphs. We propose a method to jointly embed multiple undirected graphs. Given a set of graphs, the joint embedding method identifies a linear subspace spanned by rank one symmetric matrices and projects adjacency matrices of graphs into this subspace. The projection coefficients can be treated as features of the graphs. We also propose a random graph model which generalizes classical random graph model and can be used to model multiple graphs. We show through theory and numerical experiments that under the model, the joint embedding method produces estimates of parameters with small errors. Via simulation experiments, we demonstrate that the joint embedding method produces features which lead to state of the art performance in classifying graphs. Applying the joint embedding method to human brain graphs, we find it extract interpretable features that can be used to predict individual composite creativity index.
Estimated differences: Adjusted mortality: 11.07% Regarding the number of regression parameters: Not explicitly listed, but by the following paragraph, I would suspect there are at least hundreds of regression parameters (such as an indicator of medical of school attended). "We accounted for patient characteristics, physician characteristics, and hospital fixed effects. Patient characteristics included patient age in 5-year increments (the oldest group was categorized as 95 years), sex, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, and other), primary diagnosis (Medicare Severity Diagnosis Related Group), 27 coexisting conditions (determined using the Elixhauser comorbidity index28), median annual household income estimated from residential zip codes (in deciles), an indicator variable for Medicaid coverage, and indicator variables for year. Physician characteristics included physician age in 5-year increments (the oldest group was categorized as 70 years), indicator variables for the medical schools from which the physicians graduated, and type of medical training (ie, allopathic vs osteopathic29 training)."
Timothy Amukele, an assistant professor of pathology at Johns Hopkins Medical School in Baltimore, and systems engineer Jeff Street are trying to figure out how to use drones to deliver blood samples. Timothy Amukele, an assistant professor of pathology at Johns Hopkins Medical School in Baltimore, and systems engineer Jeff Street are trying to figure out how to use drones to deliver blood samples. Three years ago, Geoff Baird bought a drone. A Seattle dad and hobby plane enthusiast, Baird used the 2.5-pound quadcopter to photograph the Hawaiian coastline and film his son's soccer and baseball games. But his big hope is that drones will soon fly tubes of blood and other specimens to Harborview Medical Center, where he works as a clinical pathologist running the hospital's chemistry and toxicology labs.
Radiologists bring home $395,000 each year, on average. In the near future, however, those numbers promise to drop to $0. Don't blame Obamacare, however, or even Trumpcare (whatever that turns out to be), but rather blame the rise of machine learning and its applicability to these two areas of medicine that are heavily focused on pattern matching, a job better done by a machine than a human. This is the argument put forward by Dr. Ziad Obermeyer of Harvard Medical School and Brigham and Women's Hospital and Ezekiel Emanuel, PhD, of the University of Pennsylvania, in an article for the New England Journal of Medicine, one of the medical profession's most prestigious journals. Machine learning will produce big winners and losers in healthcare, according to the authors, with radiologists and pathologists among the biggest losers.