conversion_table

Print conversion table based on final performance evaluation

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

conversion_table function

<dl><dt>pred_score</dt>
<dd>a vector with outcomes and final scores generated from <code>AutoScore_testing</code></dd>
<dt>by</dt>
<dd>specify correct method for categorizing the threshold: by "risk" or "score".Default to "risk"</dd>
<dt>values</dt>
<dd>A vector of threshold for analyze sensitivity, specificity and other metrics. Default to "c(0.01,0.05,0.1,0.2,0.5)"</dd></dl>

Arguments

AutoScore function: Print conversion table based on final performance evaluation — conversion_table

<dl>

<dt>pred_score</dt>
<dd>a vector with outcomes and final scores generated from <code>AutoScore_testing</code></dd>


<dt>by</dt>
<dd>specify correct method for categorizing the threshold: by "risk" or "score".Default to "risk"</dd>


<dt>values</dt>
<dd>A vector of threshold for analyze sensitivity, specificity and other metrics. Default to "c(0.01,0.05,0.1,0.2,0.5)"</dd>

</dl>

conversion_table: AutoScore function: Print conversion table based on final performance evaluation

Description

Usage

Value

Arguments

See Also