eva_performance_iauc

Internal function survival outcome: Calculate iAUC for validation set

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

eva_performance_iauc function

<dl><dt>score</dt>
<dd>Predicted score</dd>
<dt>validation_set</dt>
<dd>Dataset for generating performance</dd>
<dt>print</dt>
<dd>Whether to print out the final iAUC result</dd></dl>

Arguments

Internal function survival outcome: Calculate iAUC for validation set — eva_performance_iauc

<dl>

<dt>score</dt>
<dd>Predicted score</dd>


<dt>validation_set</dt>
<dd>Dataset for generating performance</dd>


<dt>print</dt>
<dd>Whether to print out the final iAUC result</dd>

</dl>

eva_performance_iauc: Internal function survival outcome: Calculate iAUC for validation set

Description

Usage

Arguments