plot_auc

Internal function: Make parsimony plot

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

plot_auc function

<dl><dt>AUC</dt>
<dd>A vector of AUC values (or mAUC for ordinal outcomes).</dd>
<dt>variables</dt>
<dd>A vector of variable names</dd>
<dt>num</dt>
<dd>A vector of indices for AUC values to plot. Default is to plot all.</dd>
<dt>auc_lim_min</dt>
<dd>Min y_axis limit in the parsimony plot (Default: 0.5).</dd>
<dt>auc_lim_max</dt>
<dd>Max y_axis limit in the parsimony plot (Default: "adaptive").</dd>
<dt>ylab</dt>
<dd>Title of y-axis</dd>
<dt>title</dt>
<dd>Plot title</dd></dl>

Arguments

Internal function: Make parsimony plot — plot_auc

<dl>

<dt>AUC</dt>
<dd>A vector of AUC values (or mAUC for ordinal outcomes).</dd>


<dt>variables</dt>
<dd>A vector of variable names</dd>


<dt>num</dt>
<dd>A vector of indices for AUC values to plot. Default is to plot all.</dd>


<dt>auc_lim_min</dt>
<dd>Min y_axis limit in the parsimony plot (Default: 0.5).</dd>


<dt>auc_lim_max</dt>
<dd>Max y_axis limit in the parsimony plot (Default: "adaptive").</dd>


<dt>ylab</dt>
<dd>Title of y-axis</dd>


<dt>title</dt>
<dd>Plot title</dd>

</dl>

plot_auc: Internal function: Make parsimony plot

Description

Usage

Arguments