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Updated: Nov 1, 2025

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

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Predictive analytics for step-up therapy: Supervised or semi-supervised learning?

Mohammad Amin Morid¹, Michael Lau², Guilherme Del Fiol³

¹Department of Information Systems and Analytics, Leavey School of Business, Santa Clara University, Santa Clara, CA, United States.

Journal of Biomedical Informatics

|June 19, 2021

Summary

This summary is machine-generated.

Semi-supervised learning models accurately predict rheumatoid arthritis patients needing step-up therapy, outperforming traditional supervised methods. This approach aids healthcare resource planning for chronic disease management.

Keywords:

Chronic care management Resource planning Rheumatoid arthritis Semi-supervised learning Step-up therapy

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Area of Science:

Machine Learning in Healthcare
Rheumatoid Arthritis Treatment

Background:

Step-up therapy balances medication efficacy, cost, and risk.
Predicting transitions to higher therapy lines is challenging for resource planning.

Purpose of the Study:

Compare supervised vs. semi-supervised learning for predicting rheumatoid arthritis step-up therapy.
Identify patients advancing from conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) within one year.

Main Methods:

Extracted features: demographics, medications, diagnoses, provider characteristics, procedures.
Implemented and compared supervised and semi-supervised learning models.
Conducted error analysis to understand misclassifications.

Main Results:

Semi-supervised learning (one-class SVM) achieved higher F-measure (65%) than supervised learning (XGBoost, 42%).
Semi-supervised approach showed significantly better precision and recall.
Feature importance analysis indicated all feature groups contributed to model performance.

Conclusions:

Supervised learning is suboptimal for predicting step-up therapy due to noisy negative class labels.
Semi-supervised learning offers a robust approach for clinical decisions in step-up therapy.
The developed semi-supervised method is applicable to other step-up therapy scenarios.