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.
MAP(mat = NULL, note = NULL, yes.con = FALSE, full.output = FALSE)
| mat | Count data (sparse matrix). One of the columns has to be ICD data with name being ICD. |
|---|---|
| note | Note count (sparse matrix) indicating health 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. |
Returns a list with following objects:
indicates predicted probabilities.
the cutoff value that can be used to derive binary phenotype.
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>.
## 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)#> ####################### #> MAP only considers pateints who have note count data and #> at least one nonmissing variable! #> #### #> Here is a summary of the input data: #> Total number of patients: 400 #> ICD NLP note Freq #> 1 YES YES YES 400 #> #####> 6 x 1 sparse Matrix of class "dgCMatrix" #> #> [1,] 0.9384445 #> [2,] 0.9662295 #> [3,] 0.9998454 #> [4,] 0.9999777 #> [5,] 0.9999809 #> [6,] 0.6284447res$cut.MAP#> [1] 0.3395459