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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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An interpretable machine learning approach for predicting 30-day readmission after stroke.

Ji Lv¹, Mengmeng Zhang², Yujie Fu²

¹Emergency Medicine Department of the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province 221002, China; College of Computer Science and Technology, Jilin University, Changchun, Jilin Province 130000, China.

International Journal of Medical Informatics

|March 25, 2023

Summary

This summary is machine-generated.

This study developed an interpretable machine learning model to predict 30-day stroke readmissions, identifying key risk factors like severe carotid artery stenosis and elevated homocysteine levels.

Keywords:

Machine learning Readmission SHAP Stroke

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Area of Science:

Neurology
Medical Informatics
Machine Learning in Healthcare

Background:

Stroke is a leading global cause of death with high recurrence rates.
Predicting and preventing stroke readmissions is a critical healthcare challenge.

Purpose of the Study:

To identify significant risk factors for stroke recurrence.
To develop an interpretable machine learning model for predicting 30-day stroke readmissions.

Main Methods:

Utilized electronic health records (EHRs) from 6,558 stroke patients.
Extracted 74 features, refined to the top 20 using chi-2, for model building.
Employed Shapley Additive exPlanations (SHAP) for model interpretability.

Main Results:

The final model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.80.
Identified top 10 risk factors including severe carotid artery stenosis, homocysteine, and glycosylated hemoglobin.
A 10-feature model showed comparable predictive performance to a 20-feature model.

Conclusions:

The developed machine learning model effectively predicts 30-day stroke readmissions.
Identified risk factors offer valuable insights for targeted stroke treatment and prevention strategies.