estimate_p_mat

Internal function: generate probability matrix for ordinal outcomes given
thresholds, linear predictor and link function

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

estimate_p_mat function

<dl><dt>theta</dt>
<dd>numeric vector of thresholds</dd>
<dt>z</dt>
<dd>numeric vector of linear predictor</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: generate probability matrix for ordinal outcomes given
thresholds, linear predictor and link function — estimate_p_mat

<dl>

<dt>theta</dt>
<dd>numeric vector of thresholds</dd>


<dt>z</dt>
<dd>numeric vector of linear predictor</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>

estimate_p_mat: Internal function: generate probability matrix for ordinal outcomes given thresholds, linear predictor and link function

Description

Usage

Arguments