boot_df

Sample rows with replacement from a matrix

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

boot_df function

<dl><dt>x_matrix</dt>
<dd>matrix to perform sampling on</dd>
<dt>y_sur</dt>
<dd>The numeric vector of the qualitative surrogate.</dd>
<dt>ID</dt>
<dd>The patient ID</dd>
<dt>size</dt>
<dd>size of matrix returned</dd>
<dt>seed</dt>
<dd>seed for sampling</dd>
<dt>prob</dt>
<dd>Vector for weight sampling</dd></dl>

Arguments

boot_df — boot_df

<dl>

<dt>x_matrix</dt>
<dd>matrix to perform sampling on</dd>


<dt>y_sur</dt>
<dd>The numeric vector of the qualitative surrogate.</dd>


<dt>ID</dt>
<dd>The patient ID</dd>


<dt>size</dt>
<dd>size of matrix returned</dd>


<dt>seed</dt>
<dd>seed for sampling</dd>


<dt>prob</dt>
<dd>Vector for weight sampling</dd>

</dl>

boot_df: boot_df

Description

Usage

Value

Arguments