print_performance_ci_survival

Print iAUC, c-index and time-dependent AUC as the predictive performance

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

print_performance_ci_survival function

<dl><dt>score</dt>
<dd>Predicted score</dd>
<dt>validation_set</dt>
<dd>Dataset for generating performance</dd>
<dt>time_point</dt>
<dd>The time points to be evaluated using time-dependent AUC(t).</dd>
<dt>n_boot</dt>
<dd>Number of bootstrap cycles to compute 95% CI for performance
metrics.</dd></dl>

Arguments

AutoScore function for survival outcomes: Print predictive performance with confidence intervals — print_performance_ci_survival

<dl>

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


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


<dt>time_point</dt>
<dd>The time points to be evaluated using time-dependent AUC(t).</dd>


<dt>n_boot</dt>
<dd>Number of bootstrap cycles to compute 95% CI for performance
metrics.</dd>

</dl>

print_performance_ci_survival: AutoScore function for survival outcomes: Print predictive performance with confidence intervals

Description

Usage

Value

Arguments

See Also