compute_final_score_ord

Internal function: Compute risk scores for ordinal data given variables
selected, cut-off values and scoring table

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

compute_final_score_ord function

<dl><dt>data</dt>
<dd>A processed <code>data.frame</code> that contains data for validation
or testing purpose. This <code>data.frame</code> must have variable <code>label</code>
and should have same format as <code>train_set</code> (same variable names and
outcomes)</dd>
<dt>final_variables</dt>
<dd>A vector containing the list of selected variables,
selected from Step(ii) <code>AutoScore_parsimony_Ordinal</code>.</dd>
<dt>cut_vec</dt>
<dd>Generated from STEP(iii) <code>AutoScore_weighting_Ordinal</code>.</dd>
<dt>scoring_table</dt>
<dd>The final scoring table after fine-tuning, generated
from STEP(iv) <code>AutoScore_fine_tuning_Ordinal</code>.Please follow the
guidebook</dd></dl>

Arguments

Internal function: Compute risk scores for ordinal data given variables
selected, cut-off values and scoring table — compute_final_score_ord

<dl>

<dt>data</dt>
<dd>A processed <code>data.frame</code> that contains data for validation
or testing purpose. This <code>data.frame</code> must have variable <code>label</code>
and should have same format as <code>train_set</code> (same variable names and
outcomes)</dd>


<dt>final_variables</dt>
<dd>A vector containing the list of selected variables,
selected from Step(ii) <code>AutoScore_parsimony_Ordinal</code>.</dd>


<dt>cut_vec</dt>
<dd>Generated from STEP(iii) <code>AutoScore_weighting_Ordinal</code>.</dd>


<dt>scoring_table</dt>
<dd>The final scoring table after fine-tuning, generated
from STEP(iv) <code>AutoScore_fine_tuning_Ordinal</code>.Please follow the
guidebook</dd>

</dl>

compute_final_score_ord: Internal function: Compute risk scores for ordinal data given variables selected, cut-off values and scoring table

Description

Usage

Arguments