compute_score_table_ord

Compute scoring table based on training dataset

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_score_table_ord function

<dl><dt>train_set_2</dt>
<dd>Processed training set after variable transformation</dd>
<dt>max_score</dt>
<dd>Maximum total score</dd>
<dt>variable_list</dt>
<dd>List of included variables</dd>
<dt>link</dt>
<dd>The link function used to model ordinal outcomes. Default is
<code>"logit"</code> for proportional odds model. Other options are
<code>"cloglog"</code> (proportional hazards model) and <code>"probit"</code>.</dd></dl>

Arguments

Internal function: Compute scoring table for ordinal outcomes based on
  training dataset — compute_score_table_ord

<dl>

<dt>train_set_2</dt>
<dd>Processed training set after variable transformation</dd>


<dt>max_score</dt>
<dd>Maximum total score</dd>


<dt>variable_list</dt>
<dd>List of included variables</dd>


<dt>link</dt>
<dd>The link function used to model ordinal outcomes. Default is
<code>"logit"</code> for proportional odds model. Other options are
<code>"cloglog"</code> (proportional hazards model) and <code>"probit"</code>.</dd>

</dl>

compute_score_table_ord: Internal function: Compute scoring table for ordinal outcomes based on training dataset

Description

Usage

Value

Arguments