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Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Related Experiment Video

Updated: Sep 22, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Wake-up Stroke Outcome Prediction by Interpretable Decision Tree Model.

Miloš Ajčević¹, Aleksandar Miladinović¹, Giovanni Furlanis²

¹Department of Engineering and Architecture, University of Trieste, Trieste, Italy.

Studies in Health Technology and Informatics

|May 25, 2022

Summary

Predicting outcomes for wake-up ischemic stroke (WUS) is crucial. This study developed an interpretable model using clinical and imaging data to accurately forecast good recovery in thrombolysis-treated WUS patients.

Keywords:

Classification and Regression Tree Clinical outcome Predictive modeling Wake-up stroke

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Last Updated: Sep 22, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia

A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia

Published on: August 18, 2015

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

Published on: February 22, 2020

Area of Science:

Neurology
Medical Imaging
Data Science

Background:

Accurate outcome prediction in wake-up ischemic stroke (WUS) is essential for optimizing treatment and improving patient recovery.
Guiding therapeutic strategies requires reliable prognostic tools for WUS patients receiving thrombolysis.

Purpose of the Study:

To develop an interpretable Classification and Regression Tree (CART) model for predicting good outcomes (NIHSS 7-day < 5) in thrombolysis-treated WUS patients.
To identify key clinical and neuroimaging predictors of functional recovery in WUS.

Main Methods:

Utilized a dataset of 104 WUS patients, incorporating demographic, clinical, and neuroimaging features.
Employed the Classification and Regression Tree (CART) method with a Gini split criterion to build a predictive model.
Validated the model's performance using 5-fold cross-validation.

Main Results:

The developed decision tree model identified NIHSS at admission, ischemic core volume, and age as significant predictors.
The model achieved a predictive accuracy of 86.5%.
The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) was 0.88, indicating strong discriminative ability.

Conclusions:

An interpretable CART model effectively predicts good clinical outcomes in thrombolysis-treated WUS patients.
The model integrates easily accessible clinical and neuroimaging data for prognostic assessment.
This preliminary study highlights the potential of interpretable machine learning in stroke outcome prediction.