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Updated: Oct 16, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Harnessing Machine Learning to Personalize Web-Based Health Care Content.

Ahmad Guni1,2, Pasha Normahani1,2, Alun Davies1,2

  • 1Department of Surgery and Cancer, Imperial College London, London, United Kingdom.

Journal of Medical Internet Research
|October 19, 2021
PubMed
Summary
This summary is machine-generated.

Personalized health information online is crucial for patient engagement. Machine learning (ML) can analyze big data to deliver tailored, high-quality web content, improving health outcomes and self-management.

Keywords:
internetmachine learningonline health informationpatient educationpersonalized content

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Patient Engagement Technologies

Background:

  • Web-based health information is a primary patient resource, but quality varies significantly, leading to challenges with accuracy and accessibility.
  • Personalization in healthcare models emphasizes patient needs and values, yet traditional methods for identifying high-quality content are insufficient.
  • The healthcare sector has underutilized machine learning (ML) for analyzing user and content features compared to other industries.

Purpose of the Study:

  • To explore the application of machine learning (ML) in personalizing web-based healthcare content for patients.
  • To address the limitations of traditional methods in determining high-quality content for individual users.
  • To leverage big data in healthcare for automated, large-scale personalized content recommendations.

Main Methods:

  • Utilizing machine learning algorithms to process large datasets of user and content features.
  • Integrating structured and unstructured data from comprehensive patient profiles (big data).
  • Developing predictive models that automatically improve over time for content recommendation.

Main Results:

  • Machine learning enables the analysis of user and content features to automate personalized content recommendations.
  • Big data in healthcare facilitates the creation of comprehensive patient profiles for tailored content delivery.
  • ML can predict content effectiveness and engagement for individual patients.

Conclusions:

  • Machine learning offers a scalable solution for personalizing web-based health information, overcoming quality and accessibility issues.
  • Personalized content delivery through ML can enhance patient engagement, education, and self-management.
  • The integration of ML and big data in healthcare has the potential to significantly improve clinical outcomes.