Automatic Phenotyping of Electronic Health Record at Visit Resolution
boot_df
build_qantsur
build_quali
check_arg_test_phevis
check_arg_train_phevis
cum_lag
expcorrectC
fct_surrogate_quanti
ggindividual_plot
matrix_exp_smooth
noising
norm_var
PheVis: Automatic Phenotyping of Electronic Health Record at Visit Res...
phenorm_longit_fit
phenorm_longit_simpl
pred_lme4model
pretty_cv.glmnet
roll_time_sum
rolling_var
safe_selection
sur_exp_smooth
test_phevis
train_phevis
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 described in preprint by Ferté et al. (2020) <doi:10.1101/2020.06.15.20131458>.