Package 'MAP'

Title: Multimodal Automated Phenotyping
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>.).
Authors: 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 <[email protected]>
License: GPL-3
Version: 1.0.0
Built: 2025-01-08 21:31:55 UTC
Source: https://github.com/celehs/map

Help Index

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.

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.

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.

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)