compute_prob_observed

Internal function: Based on given labels and scores, compute proportion of
subjects observed in each outcome category in given score intervals.

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_prob_observed function

<dl><dt>pred_score</dt>
<dd>A <code>data.frame</code> with outcomes and final scores
generated from <code>AutoScore_fine_tuning_Ordinal</code></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>
<dt>max_score</dt>
<dd>Maximum attainable value of final scores.</dd>
<dt>score_breaks</dt>
<dd>A vector of score breaks to group scores. The average
predicted risk will be reported for each score interval in the lookup
table. Users are advised to first visualise the predicted risk for all
attainable scores to determine <code>scores</code> (see
<code>plot_predicted_risk</code>)</dd></dl>

Arguments

Internal function: Based on given labels and scores, compute proportion of
subjects observed in each outcome category in given score intervals. — compute_prob_observed

<dl>

<dt>pred_score</dt>
<dd>A <code>data.frame</code> with outcomes and final scores
generated from <code>AutoScore_fine_tuning_Ordinal</code></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>


<dt>max_score</dt>
<dd>Maximum attainable value of final scores.</dd>


<dt>score_breaks</dt>
<dd>A vector of score breaks to group scores. The average
predicted risk will be reported for each score interval in the lookup
table. Users are advised to first visualise the predicted risk for all
attainable scores to determine <code>scores</code> (see
<code>plot_predicted_risk</code>)</dd>

</dl>

compute_prob_observed: Internal function: Based on given labels and scores, compute proportion of subjects observed in each outcome category in given score intervals.

Description

Usage

Arguments