check_data

AutoScore function for datasets with binary outcomes: Check whether the input dataset fulfill the requirement of the AutoScore

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

check_data function

<dl><dt>data</dt>
<dd>The data to be checked</dd></dl>

Arguments

AutoScore function for datasets with binary outcomes: Check whether the input dataset fulfill the requirement of the AutoScore — check_data

<dl>

<dt>data</dt>
<dd>The data to be checked</dd>

</dl>

check_data: AutoScore function for datasets with binary outcomes: Check whether the input dataset fulfill the requirement of the AutoScore

Description

Usage

Value

Arguments

Examples