sample_data_with_missing

20000 simulated samples with missing values, which can be used for demostrating AutoScore workflow dealing with missing values.<ul>
<li>Johnson, A., Pollard, T., Shen, L. et al. MIMIC-III, a freely accessible critical care database. Sci Data 3, 160035 (2016).</li>
</ul>

datasets

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

sample_data_with_missing function

An object of class <code>data.frame</code> with 20000 rows and 23 columns.

Format

20000 simulated ICU admission data with missing values — sample_data_with_missing

20000 simulated ICU admission data with missing values

sample_data_with_missing: 20000 simulated ICU admission data with missing values

Description

Usage

Arguments

Format