induce_median_missing

Internal function: induce informative missing in a single variable

A novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The details are described in our research paper<doi:10.2196/21798>. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies.

Feng Xie

AutoScore

An Interpretable Machine Learning-Based Automatic Clinical Score
Generator

Yilin Ning

Han Yuan

Mingxuan Liu

Siqi Li

Ehsan Saffari

Bibhas Chakraborty

Nan Liu

induce_median_missing function

<dl><dt>x</dt>
<dd>Variable to induce missing in.</dd>
<dt>prop_missing</dt>
<dd>Proportion of missing to induce for each
<code>vars_to_induce</code>. Can be a single value for a common proportion for
all variables (default is 0.4), or a vector with same length as
<code>vars_to_induce</code>.</dd></dl>

Arguments

Internal function: induce informative missing in a single variable — induce_median_missing

<dl>

<dt>x</dt>
<dd>Variable to induce missing in.</dd>


<dt>prop_missing</dt>
<dd>Proportion of missing to induce for each
<code>vars_to_induce</code>. Can be a single value for a common proportion for
all variables (default is 0.4), or a vector with same length as
<code>vars_to_induce</code>.</dd>

</dl>

induce_median_missing: Internal function: induce informative missing in a single variable

Description

Usage

Arguments