| Type: | Package |
| Title: | Multimodal Automated Phenotyping |
| Version: | 1.0.0 |
| Description: | Electronic health records (EHR) linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. Towards that end, we developed an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP). Specifically, our proposed method, called MAP (Map Automated Phenotyping algorithm), fits an ensemble of latent mixture models on aggregated ICD and NLP counts along with healthcare utilization. The MAP algorithm yields a predicted probability of phenotype for each patient and a threshold for classifying subjects with phenotype yes/no (See Katherine P. Liao, et al. (2019) <doi:10.1093/jamia/ocz066>.). |
| URL: | https://celehs.github.io/MAP/ |
| BugReports: | https://github.com/celehs/MAP/issues |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Depends: | R (≥ 3.4.0), flexmix (≥ 2.3-14), Matrix(≥ 1.2-10) |
| Imports: | magrittr |
| Suggests: | knitr, pROC, rmarkdown, testthat |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| RoxygenNote: | 7.3.2 |
| Packaged: | 2024-12-02 14:38:25 UTC; root |
| Author: | Thomas Charlon 
[aut, cre],
Chuan Hong [aut],
Jiehuan Sun [aut],
Katherine Liao [aut],
Sheng Yu [aut],
Tianxi Cai [aut],
PARSE Health [aut] (<https://parse-health.org>),
CELEHS [aut] (<https://celehs.hms.harvard.edu>) |
| Maintainer: | Thomas Charlon <charlon@protonmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-12-02 17:10:01 UTC |
Exposition pipe
Description
Expose the names in 'lhs' to the 'rhs' expression. Magrittr imported function, see details and examples in the magrittr package.
Arguments
lhs |
A list, environment, or a data.frame. |
rhs |
An expression where the names in lhs is available. |
Value
Result of rhs applied to one or several names of lhs.
Pipe
Description
Pipe an object forward into a function or call expression. Magrittr imported function, see details and examples in the magrittr package.
Arguments
lhs |
A value or the magrittr placeholder. |
rhs |
A function call using the magrittr semantics. |
Value
Result of rhs applied to lhs, see details in magrittr package.
Assignment pipe
Description
Pipe an object forward into a function or call expression and update the 'lhs' object with the resulting value. Magrittr imported function, see details and examples in the magrittr package.
Arguments
lhs |
An object which serves both as the initial value and as target. |
rhs |
a function call using the magrittr semantics. |
Value
None, used to update the value of lhs.
MAP algorithm
Description
Main function to perform MAP algorithm to calculate predicted probabilities of positive phenotype for each patient based on NLP and ICD counts adjusted for healthcare utilization. For large number of patients (>50k) it may take very long to compute, so a subset_sample parameter is provided to perform the fit on a subset of patients and project the remaining. The subset_sample_size controls the maximum number of patients on which to perform the fit.
Usage
MAP(
mat = NULL,
note = NULL,
yes.con = FALSE,
full.output = FALSE,
subset_sample = FALSE,
subset_sample_size = 5000,
verbose = TRUE
)
Arguments
mat |
Count data (sparse matrix). One of the columns has to be ICD data with name being ICD. |
note |
Note count (sparse matrix) indicating healthcare utilization. |
yes.con |
A logical variable indicating if concomitant is desired. Not used for now. |
full.output |
A logical variable indicating if full outputs are desired. |
subset_sample |
Logical, perform fit on a subset of patients and project remaining. |
subset_sample_size |
If subset_sample TRUE, number of patients on which to perform the fit (default 50k). |
verbose |
Print model information |
Value
Returns a list with following objects:
scores |
Indicates predicted probabilities. |
cut.MAP |
The cutoff value that can be used to derive binary phenotypes. |
References
High-throughput Multimodal Automated Phenotyping (MAP) with Application to PheWAS. Katherine P. Liao, Jiehuan Sun, Tianrun A. Cai, Nicholas Link, Chuan Hong, Jie Huang, Jennifer Huffman, Jessica Gronsbell, Yichi Zhang, Yuk-Lam Ho, Victor Castro, Vivian Gainer, Shawn Murphy, Christopher J. O’Donnell, J. Michael Gaziano, Kelly Cho, Peter Szolovits, Isaac Kohane, Sheng Yu, and Tianxi Cai with the VA Million Veteran Program (2019) <doi:10.1101/587436>.
Examples
## simulate data to test the algorithm
n = 400
ICD = c(rpois(n/4,10), rpois(n/4,1), rep(0,n/2) )
NLP = c(rpois(n/4,10), rpois(n/4,1), rep(0,n/2) )
mat = Matrix(data=cbind(ICD,NLP),sparse = TRUE)
note = Matrix(rpois(n,10)+5,ncol=1,sparse = TRUE)
res = MAP(mat = mat, note=note)
head(res$scores)
res$cut.MAP
MAP dictionary
Description
MAP dictionary that maps phecode to CUIs
Usage
phecode.cuis.list
Format
A list of 1866
Examples
head(phecode.cuis.list)
tail(phecode.cuis.list)