| Type: | Package |
| Title: | Multisource Graph Synthesis with EHR Data |
| Version: | 0.1.0 |
| Description: | We develop Multi-source Graph Synthesis (MUGS), an algorithm designed to create embeddings for pediatric Electronic Health Record (EHR) codes by leveraging graphical information from three distinct sources: (1) pediatric EHR data, (2) EHR data from the general patient population, and (3) existing hierarchical medical ontology knowledge shared across different patient populations. See Li et al. (2024) <doi:10.1038/s41746-024-01320-4> for details. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| LazyDataCompression: | xz |
| RoxygenNote: | 7.3.2 |
| URL: | https://github.com/celehs/MUGS, https://celehs.github.io/MUGS/, https://doi.org/10.1038/s41746-024-01320-4 |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Imports: | MASS, Matrix, fastDummies, doSNOW, dplyr, grplasso, foreach, glmnet, grpreg, inline, mvtnorm, pROC, parallel, RcppArmadillo, rsvd, methods |
| Depends: | R (≥ 3.5.0) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2025-05-15 03:56:15 UTC; User1 |
| Author: | Mengyan Li [cre, aut], Thomas Charlon [ctb] (ORCID: 0000-0001-7497-0470), Xiaoou Li [aut], Tianxi Cai [aut], PARSE LTD [aut], CELEHS Team [aut] |
| Maintainer: | Mengyan Li <mengyanli@bentley.edu> |
| Repository: | CRAN |
| Date/Publication: | 2025-05-19 13:40:09 UTC |
Function Used To Estimate Code Effects
Description
This function estimates code effects using left and right embeddings from source and target sites.
Usage
CodeEff_Matrix(
S.1,
S.2,
n1,
n2,
U.1,
U.2,
V.1,
V.2,
common_codes,
zeta.int,
lambda,
p
)
Arguments
S.1 |
SPPMI from the source site. |
S.2 |
SPPMI from the target site. |
n1 |
The number of codes from the source site. |
n2 |
The number of codes from the target site. |
U.1 |
The left embeddings left singular vectors times the square root of the singular values from the source site. |
U.2 |
The left embeddings left singular vectors times the square root of the singular values from the target site. |
V.1 |
The right embeddings right singular vectors times the square root of the singular values from the source site. |
V.2 |
The right embeddings right singular vectors times the square root of the singular values from the target site. |
common_codes |
The list of overlapping codes. |
zeta.int |
The initial estimator for the code effects. |
lambda |
The tuning parameter controls the intensity of penalization on the code effect. |
p |
The length of an embedding. |
Value
A list with the following elements:
zeta |
The estimated code effects. |
dif_F |
The Frobenius norm difference between the updated and initial estimators. |
V.1.new |
Updated right embeddings for the source site. |
V.2.new |
Updated right embeddings for the target site. |
Function Used To Estimate Code-Site Effects Parallelly
Description
Function Used To Estimate Code-Site Effects Parallelly
Usage
CodeSiteEff_l2_par(
S.1,
S.2,
n1,
n2,
U.1,
U.2,
V.1,
V.2,
delta.int,
lambda.delta,
p,
common_codes,
n.common,
n.core
)
Arguments
S.1 |
SPPMI from the source site |
S.2 |
SPPMI from the target site |
n1 |
the number of codes from the source site |
n2 |
the number of codes from the target site |
U.1 |
the left embeddings (left singular vectors times the square root of the singular values) from the source site |
U.2 |
the left embeddings (left singular vectors times the square root of the singular values) from the target site |
V.1 |
the right embeddings (right singular vectors times the square root of the singular values) from the source site |
V.2 |
the right embeddings (right singular vectors times the square root of the singular values) from the target site |
delta.int |
the initial estimator for the code-site effect |
lambda.delta |
the tuning parameter controls the intensity of penalization on the code-site effects |
p |
the length of an embedding |
common_codes |
the list of overlapping codes |
n.common |
the number of overlapping codes |
n.core |
the number of cored used for parallel computation |
Value
The output for the estimation of code-site effects
Function used to generate input data (used only for Simulations) Generate SPPMIs, dummy matrices based on prior group structures, and code-code pairs for tuning and evaluation
Description
Function used to generate input data (used only for Simulations) Generate SPPMIs, dummy matrices based on prior group structures, and code-code pairs for tuning and evaluation
Usage
DataGen_rare_group(
seed = NULL,
p,
n1,
n2,
n.common,
n.group,
sigma.eps.1,
sigma.eps.2,
ratio.delta,
network.k,
rho.beta,
rho.U0,
rho.delta,
sigma.rare,
n.rare,
group.size
)
Arguments
seed |
for reproducibility |
p |
the length of an embedding |
n1 |
the number of codes in site 1 |
n2 |
the number of codes in site 2 |
n.common |
common: the number of overlapping codes |
n.group |
the number of groups |
sigma.eps.1 |
the sd of error in site 1 |
sigma.eps.2 |
the sd of error in site 2 |
ratio.delta |
the proportion of codes in each site that have site-specific effects applied to them |
network.k |
the number of distinct blocks within each site for which unique inter-code correlations are modeled |
rho.beta |
AR parameter for the group effects covariance matrix |
rho.U0 |
AR parameter for the code effects covariance matrix |
rho.delta |
AR parameter for the code-site effects covariance matrix |
sigma.rare |
the sd of error for rare codes (usually larger than sigma.eps.1 and sigma.eps.2) |
n.rare |
The number of rare codes |
group.size |
the size of each group |
Value
Returns input data, SPPMIs, dummy matrices based on prior group structures and code-code pairs for tuning and evaluation
Function Used To Estimate Group Effects Parallelly
Description
Function Used To Estimate Group Effects Parallelly
Usage
GroupEff_par(
S.MGB,
S.BCH,
n.MGB,
n.BCH,
U.MGB,
U.BCH,
V.MGB,
V.BCH,
X.MGB.group,
X.BCH.group,
n.group,
name.list,
beta.int,
lambda = 0,
p,
n.core
)
Arguments
S.MGB |
SPPMI from the source site |
S.BCH |
SPPMI from the target site |
n.MGB |
the number of codes from the source site |
n.BCH |
the number of codes from the target site |
U.MGB |
the left embeddings (left singular vectors times the square root of the singular values) from the source site |
U.BCH |
the left embeddings (left singular vectors times the square root of the singular values) from the target site |
V.MGB |
the right embeddings (right singular vectors times the square root of the singular values) from the source site |
V.BCH |
the right embeddings (right singular vectors times the square root of the singular values) from the target site |
X.MGB.group |
the dummy matrix based on prior group structures at the source site |
X.BCH.group |
the dummy matrix based on prior group structures at the target site |
n.group |
the number of groups |
name.list |
the full list of code names from the source site and the target site with repeated names of overlapping codes |
beta.int |
the initial estimator for the group effects |
lambda |
the tuning parameter controls the intensity of penalization on the group effect; by default we set it to 0 |
p |
the length of an embedding |
n.core |
the number of cored used for parallel computation |
Value
The output of estimating group effects parallelly
Main function for MUGS algorithm
Description
Main function for MUGS algorithm
Usage
MUGS(
TUNE = FALSE,
Eva = TRUE,
Lambda = c(10),
Lambda.delta = c(1000),
n.core = 4,
tol = 1,
seed = NULL,
S.1 = NULL,
S.2 = NULL,
X.group.source = NULL,
X.group.target = NULL,
pairs.rel.CV = NULL,
pairs.rel.EV = NULL,
p = 100,
n.group = 400,
outdir = NULL
)
Arguments
TUNE |
Logical value indicating whether the function should tune parameters TRUE or use predefined parameters FALSE. |
Eva |
Logical value indicating whether to perform evaluation (TRUE) or skip it (FALSE). |
Lambda |
The candidate values for the tuning parameter controlling the intensity of penalization on the code effects. |
Lambda.delta |
The candidate values for the tuning parameter controlling the intensity of penalization on the code-site effects. |
n.core |
Integer specifying the number of cores to use for parallel processing. |
tol |
Numeric value representing the tolerance level for convergence in the algorithm. |
seed |
Integer used to set the seed for random number generation, ensuring reproducibility. Set to NULL to disable. |
S.1 |
The SPPMI matrix from site 1. |
S.2 |
The SPPMI matrix from site 2. |
X.group.source |
The dummy matrix representing the group structure of codes at site 1. |
X.group.target |
The dummy matrix representing the group structure of codes at site 2. |
pairs.rel.CV |
Code-code pairs used for tuning via cross-validation. |
pairs.rel.EV |
Code-code pairs used for evaluation. |
p |
Integer indicating the length of embeddings. |
n.group |
The number of groups. |
outdir |
Optional directory to write output files. Defaults to a temporary directory. |
Value
A list or saved files containing the embedding matrices, similarity matrices, and site-heterogeneous code analysis.
S.1 Dataset
Description
A matrix containing SPPMI data from the source site. This dataset is used as input for analysis in the package.
Usage
S.1
Format
A matrix with 2000 rows and 10 columns:
- Row Names
Unique identifiers for each row.
- Columns
Numeric values representing SPPMI data.
S.2 Dataset
Description
A matrix containing SPPMI data from the target site. This dataset is used as input for analysis in the package.
Usage
S.2
Format
A matrix with 2000 rows and 10 columns:
- Row Names
Unique identifiers for each row.
- Columns
Numeric values representing SPPMI data.
U.1 Dataset
Description
A matrix containing left embeddings from the source site. These embeddings are used for embedding-based computations.
Usage
U.1
Format
A matrix with 2000 rows and 10 columns:
- Row Names
Unique identifiers for each row.
- Columns
Numeric values representing embeddings.
U.2 Dataset
Description
A matrix containing left embeddings from the target site. These embeddings are used for embedding-based computations.
Usage
U.2
Format
A matrix with 2000 rows and 10 columns:
- Row Names
Unique identifiers for each row.
- Columns
Numeric values representing embeddings.
X.group.source Dataset
Description
A matrix containing group structures at the source site. It represents binary group membership of entities at the source.
Usage
X.group.source
Format
A matrix with 2000 rows and 50 columns:
- Rows
Entities at the source site.
- Columns
Binary values (0 or 1) indicating group membership.
X.group.target Dataset
Description
A matrix containing group structures at the target site. It represents binary group membership of entities at the target.
Usage
X.group.target
Format
A matrix with 2000 rows and 50 columns:
- Rows
Entities at the target site.
- Columns
Binary values (0 or 1) indicating group membership.
Download and Load Example Data from Zenodo
Description
Download and Load Example Data from Zenodo
Usage
download_example_data(file, destdir = tempdir())
Arguments
file |
Name of the .Rdata file to download (e.g., "S.1.Rdata"). |
destdir |
Directory to store the downloaded data. Defaults to a temporary directory. |
Value
A list containing the loaded dataset.
Function Used For Tuning And Evaluation
Description
Function Used For Tuning And Evaluation
Usage
evaluation.sim(pairs.rel, U, seed = NULL)
Arguments
pairs.rel |
the known code-code pairs |
U |
the code embedding matrix |
seed |
Optional integer for reproducibility of sampling. |
Value
The output of tuning and evaluation
Function For Getting Embedding From SVD
Description
Function For Getting Embedding From SVD
Usage
get_embed(mysvd, d = 2000, normalize = TRUE)
Arguments
mysvd |
the (managed) svd result (adding an element with 'names') |
d |
dim of the final embedding |
normalize |
if the output embeddings have l2 norm equal to 1 |
Value
The embedding from SVD
pairs.rel.CV Dataset
Description
A data frame containing cross-validation pairs for relative comparisons.
Usage
pairs.rel.CV
Format
A data frame with multiple columns:
- col
Integer representing the column index of a pair.
- row
Integer representing the row index of a pair.
- type
Character string indicating the type of data (e.g., "train", "test").
pairs.rel.EV Dataset
Description
A data frame containing evaluation pairs for relative comparisons.
Usage
pairs.rel.EV
Format
A data frame with multiple columns:
- col
Integer representing the column index of a pair.
- row
Integer representing the row index of a pair.
- type
Character string indicating the type of data (e.g., "validation").