MAP {MAP}

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.

Usage

MAP(mat = NULL, note = NULL, yes.con = FALSE, full.output = FALSE)

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 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.

Value

Returns a list with following objects:

scores

indicates predicted probabilities.

cut.MAP

the cutoff value that can be used to derive binary phenotype.

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