Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Lorenzi, Elizabeth C"'
We aim to create a framework for transfer learning using latent factor models to learn the dependence structure between a larger source dataset and a target dataset. The methodology is motivated by our goal of building a risk-assessment model for sur
Externí odkaz:
http://arxiv.org/abs/1612.00555
We develop a novel algorithm, Predictive Hierarchical Clustering (PHC), for agglomerative hierarchical clustering of current procedural terminology (CPT) codes. Our predictive hierarchical clustering aims to cluster subgroups, not individual observat
Externí odkaz:
http://arxiv.org/abs/1604.07031
Autor:
Lorenzi, Elizabeth C.
A mixed effects multinomial logistic model is useful in understanding a response variable with more than two outcomes and its relationship with covariates for nested data sets. Because of the nested structure of the data, using random intercepts is n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59b3343e4637638eddf25c56696bec0e
Autor:
Crawford AM; Berry Consultants LLC, Austin, TX (A.M.C., E.C.L., R.J.L.)., Lorenzi EC; Berry Consultants LLC, Austin, TX (A.M.C., E.C.L., R.J.L.)., Saville BR; Adaptix Trials LLC, Austin, TX (B.R.S.).; Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN (B.R.S.)., Lewis RJ; Berry Consultants LLC, Austin, TX (A.M.C., E.C.L., R.J.L.).; Department of Emergency Medicine, Harbor-UCLA Medical Center, Torrance, CA (R.J.L.)., Anderson CS; George Institute for Global Health, University of New South Wales, Sydney, Australia (C.S.A.).; Institute for Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (C.S.A.).
Publikováno v:
Stroke [Stroke] 2024 Nov; Vol. 55 (11), pp. 2731-2741. Date of Electronic Publication: 2024 Oct 22.
