phenorm_longit_fit

Apply simplified 'PheNorm' algorithm on longitudinal data with bootstrap and noise.

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

phenorm_longit_fit function

<dl><dt>x_matrix</dt>
<dd>x matrix to sample, noise and predict on</dd>
<dt>y_sur</dt>
<dd>surrogate with 3 values (0 and 1 the extremes and 3 middle patients)</dd>
<dt>ID</dt>
<dd>Vector of patient ID</dd>
<dt>size</dt>
<dd>size of sampling. default is 10^5</dd>
<dt>seed</dt>
<dd>seed. default is 1.</dd>
<dt>p.noise</dt>
<dd>noise probability parameter. default is .3.</dd>
<dt>do_sampling</dt>
<dd>should algorithm do sampling. default is TRUE.</dd>
<dt>do_noise</dt>
<dd>should algorithm do noise. default is TRUE.</dd>
<dt>prob</dt>
<dd>sampling probability during noising denoising step</dd>
<dt>calc.prob</dt>
<dd>should the `prob` argument be calculated</dd>
<dt>nAGQ</dt>
<dd>glmer parameter</dd>
<dt>glmer.control</dt>
<dd>glmer parameter</dd></dl>

Arguments

phenorm_longit_fit — phenorm_longit_fit

<dl>

<dt>x_matrix</dt>
<dd>x matrix to sample, noise and predict on</dd>


<dt>y_sur</dt>
<dd>surrogate with 3 values (0 and 1 the extremes and 3 middle patients)</dd>


<dt>ID</dt>
<dd>Vector of patient ID</dd>


<dt>size</dt>
<dd>size of sampling. default is 10^5</dd>


<dt>seed</dt>
<dd>seed. default is 1.</dd>


<dt>p.noise</dt>
<dd>noise probability parameter. default is .3.</dd>


<dt>do_sampling</dt>
<dd>should algorithm do sampling. default is TRUE.</dd>


<dt>do_noise</dt>
<dd>should algorithm do noise. default is TRUE.</dd>


<dt>prob</dt>
<dd>sampling probability during noising denoising step</dd>


<dt>calc.prob</dt>
<dd>should the `prob` argument be calculated</dd>


<dt>nAGQ</dt>
<dd>glmer parameter</dd>


<dt>glmer.control</dt>
<dd>glmer parameter</dd>

</dl>

phenorm_longit_fit: phenorm_longit_fit

Description

Usage

Value

Arguments