check_arg_train_phevis

Function to check arguments passed to 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>.

Thomas Ferte

PheVis

Automatic Phenotyping of Electronic Health Record at Visit
Resolution

Boris P. Hejblum

check_arg_train_phevis function

<dl><dt>half_life</dt>
<dd>Duration of cumulation. For a chronic disease you might chose Inf, for acute disease you might chose the duration of the disease.</dd>
<dt>df</dt>
<dd><code>data.frame</code> containing all the variables.</dd>
<dt>START_DATE</dt>
<dd>Column name of the time column. The time column should be numeric</dd>
<dt>PATIENT_NUM</dt>
<dd>Column name of the patient id column.</dd>
<dt>ENCOUNTER_NUM</dt>
<dd>Column name of the encounter id column.</dd>
<dt>var_vec</dt>
<dd>Explanatory variables used for the prediction, including the main variables.</dd>
<dt>main_icd</dt>
<dd>Character vector of the column names of the main ICD codes.</dd>
<dt>main_cui</dt>
<dd>Character vector of the column names of the main CUIs.</dd>
<dt>rf</dt>
<dd>should pseudo-labellisation with random forest be used (default is true)</dd>
<dt>p.noise</dt>
<dd>percentage of noise introduced during the noising step (default is 0.3)</dd>
<dt>bool_SAFE</dt>
<dd>A boolean. If TRUE, SAFE selection is done, else it is not (default is TRUE)</dd>
<dt>omega</dt>
<dd>Constant for the extrema population definition (default is 2)</dd>
<dt>GS</dt>
<dd>Character string corresponding to the name of the gold-standard variable (default is null for which a vector of 0 will be taken).</dd></dl>

Arguments

check_arg_train_phevis — check_arg_train_phevis

<dl>

<dt>half_life</dt>
<dd>Duration of cumulation. For a chronic disease you might chose Inf, for acute disease you might chose the duration of the disease.</dd>


<dt>df</dt>
<dd><code>data.frame</code> containing all the variables.</dd>


<dt>START_DATE</dt>
<dd>Column name of the time column. The time column should be numeric</dd>


<dt>PATIENT_NUM</dt>
<dd>Column name of the patient id column.</dd>


<dt>ENCOUNTER_NUM</dt>
<dd>Column name of the encounter id column.</dd>


<dt>var_vec</dt>
<dd>Explanatory variables used for the prediction, including the main variables.</dd>


<dt>main_icd</dt>
<dd>Character vector of the column names of the main ICD codes.</dd>


<dt>main_cui</dt>
<dd>Character vector of the column names of the main CUIs.</dd>


<dt>rf</dt>
<dd>should pseudo-labellisation with random forest be used (default is true)</dd>


<dt>p.noise</dt>
<dd>percentage of noise introduced during the noising step (default is 0.3)</dd>


<dt>bool_SAFE</dt>
<dd>A boolean. If TRUE, SAFE selection is done, else it is not (default is TRUE)</dd>


<dt>omega</dt>
<dd>Constant for the extrema population definition (default is 2)</dd>


<dt>GS</dt>
<dd>Character string corresponding to the name of the gold-standard variable (default is null for which a vector of 0 will be taken).</dd>

</dl>

check_arg_train_phevis: check_arg_train_phevis

Description

Usage

Value

Arguments