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Using Electronic Health Record (EHR) is difficult because most of the time the true characteristic of the patient is not available. Instead we can retrieve the International Classification of Disease code related to the disease of interest or we can count the occurrence of the Unified Medical Language System. None of them is the true phenotype which needs chart review to identify. However chart review is time consuming and costly. PheVis is an algorithm which is phenotyping (i.e identify a characteristic) at the visit level in an unsupervised fashion. It can be used for chronic or acute diseases.

An example of how to use PheVis is available in the vignette. Basically there are two functions that are to be used: train_phevis which trains the algorithm and test_phevis which get the predicted probabilities.

The detailed method is available in the paper preprint proposed by Ferté et al (2020).

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Version

Install

install.packages('PheVis')

Monthly Downloads

215

Version

1.0.4

License

GPL (>= 2)

Maintainer

Thomas Ferte

Last Published

October 20th, 2023

Functions in PheVis (1.0.4)

matrix_exp_smooth

matrix_exp_smooth
noising

noising
train_phevis

train_phevis
pretty_cv.glmnet

pretty_cv.glmnet
ggindividual_plot

ggindividual_plot
safe_selection

safe_selection
rolling_var

rolling_var
roll_time_sum

roll_time_sum
sur_exp_smooth

sur_exp_smooth
phenorm_longit_fit

phenorm_longit_fit
test_phevis

test_phevis
build_qantsur

build_qantsur
check_arg_test_phevis

check_arg_test_phevis
data_phevis

PheVis simulated dataset
check_arg_train_phevis

check_arg_train_phevis
boot_df

boot_df
cum_lag

cum_lag
build_quali

build_quali
expcorrectC

expcorrectC
norm_var

norm_var
pred_lme4model

pred_lme4model
fct_surrogate_quanti

fct_surrogate_quanti
phenorm_longit_simpl

phenorm_longit_simpl
data_perf

Control data for test
PheVis-package

PheVis: Automatic Phenotyping of Electronic Health Record at Visit Resolution