Surrogate-guided ensemble Latent Dirichlet Allocation (sureLDA) is a label-free multidimensional phenotyping method. It first uses the PheNorm or MAP algorithm to initialize probabilities based on two surrogate features for each target disease, and then leverages these probabilities to guide the LDA topic model to generate phenotype-specific topics. Finally, it combines phenotype-feature counts with surrogates via clustering ensemble to yield final phenotype probabilities.
See Ahuja et al. JAMIA (2020) for details.
If devtools
is not installed, uncomment the code below and install it from CRAN.
# install.packages("devtools")
Run the code below to install sureLDA
from GitHub:
devtools::install_github("celehs/sureLDA")
Click HERE to view a demo with a simulated example.
Y. Ahuja, D. Zhou, Z. He, J. Sun, V. M. Castro, V. Gainer, S. N. Murphy, C. Hong, T. Cai. sureLDA: A Multi-Disease Automated Phenotyping Method for the Electronic Health Record. J Am Med Inform Assoc (2020); 27(8): 1235-1243
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.