plot_predicted_risk

AutoScore function for binary and ordinal outcomes: Plot predicted risk

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_predicted_risk function

<dl><dt>pred_score</dt>
<dd>Output from <code>AutoScore_testing</code> (for binary
outcomes) or <code>AutoScore_testing_Ordinal</code> (for ordinal
outcomes).</dd>
<dt>link</dt>
<dd>(For ordinal outcome only) The link function used in ordinal
regression, which must be the same as the value used to build the risk
score. Default is <code>"logit"</code> for proportional odds model.</dd>
<dt>max_score</dt>
<dd>Maximum total score (Default: 100).</dd>
<dt>final_variables</dt>
<dd>A vector containing the list of selected variables,
selected from Step(ii) <code>AutoScore_parsimony</code> (for binary
outcomes) or <code>AutoScore_parsimony_Ordinal</code> (for ordinal
outcomes).</dd>
<dt>scoring_table</dt>
<dd>The final scoring table after fine-tuning, generated
from STEP(iv) <code>AutoScore_fine_tuning</code> (for binary outcomes) or
<code>AutoScore_fine_tuning_Ordinal</code> (for ordinal outcomes).</dd>
<dt>point_size</dt>
<dd>Size of points in the plot. Default is 0.5.</dd></dl>

Arguments

AutoScore function for binary and ordinal outcomes: Plot predicted risk — plot_predicted_risk

<dl>

<dt>pred_score</dt>
<dd>Output from <code>AutoScore_testing</code> (for binary
outcomes) or <code>AutoScore_testing_Ordinal</code> (for ordinal
outcomes).</dd>


<dt>link</dt>
<dd>(For ordinal outcome only) The link function used in ordinal
regression, which must be the same as the value used to build the risk
score. Default is <code>"logit"</code> for proportional odds model.</dd>


<dt>max_score</dt>
<dd>Maximum total score (Default: 100).</dd>


<dt>final_variables</dt>
<dd>A vector containing the list of selected variables,
selected from Step(ii) <code>AutoScore_parsimony</code> (for binary
outcomes) or <code>AutoScore_parsimony_Ordinal</code> (for ordinal
outcomes).</dd>


<dt>scoring_table</dt>
<dd>The final scoring table after fine-tuning, generated
from STEP(iv) <code>AutoScore_fine_tuning</code> (for binary outcomes) or
<code>AutoScore_fine_tuning_Ordinal</code> (for ordinal outcomes).</dd>


<dt>point_size</dt>
<dd>Size of points in the plot. Default is 0.5.</dd>

</dl>

plot_predicted_risk: AutoScore function for binary and ordinal outcomes: Plot predicted risk

Description

Usage

Arguments