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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.

Jenish Maharjan¹, Yasha Ektefaie¹, Logan Ryan¹

¹Dascena, Inc., Houston, TX, United States.

Frontiers in Neurology

|February 11, 2022

Summary

This summary is machine-generated.

Machine learning algorithms can predict near-term ischemic stroke risk in hospitalized patients. This aids in identifying high-risk individuals for clinical trials and stroke prevention treatments.

Keywords:

anticoagulant therapy artificial intelligence clinical trial machine learning stroke prediction

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A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia

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Published on: August 18, 2015

Area of Science:

Neurology
Medical Informatics
Biostatistics

Background:

Stroke is a leading global cause of mortality.
Clinical trials for stroke therapies require efficient patient identification for timely enrollment.
Machine learning (ML) offers potential to enhance prediction of stroke risk.

Purpose of the Study:

To develop and evaluate a machine learning algorithm (MLA) for predicting ischemic stroke risk.
To identify hospitalized patients at high risk of stroke within a one-year timeframe.
To improve patient selection for stroke prevention and treatment clinical trials.

Main Methods:

Retrospective study of 41,970 ischemic stroke patient encounters.
Utilized electronic health records to train a gradient boosted MLA (XGBoost).
Predicted ischemic stroke occurrence from 1 day to 1 year post-encounter.

Main Results:

XGBoost achieved high performance with specificity 0.793, PPV 0.194, NPV 0.985, and AUROC 0.88.
Logistic Regression and multilayer perceptron models showed comparable AUROCs (0.862).
Key predictors included prior stroke, age, and mean systolic blood pressure.

Conclusions:

MLAs can accurately predict near-term ischemic stroke risk in hospitalized individuals.
This risk stratification tool can optimize clinical trial design for stroke prevention.
High-risk patient identification enhances potential benefit from novel stroke treatments.