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Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Updated: Jun 18, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Web-Based Personalized Machine Learning Recommendations to Enhance Shared Decision-Making in Prostate-Specific

Yi-Ting Lin1,2, Yen Chun Huang3, Chih Kuang Liu4

  • 1Department of Urology, St. Joseph's Hospital, Yunlin, Taiwan.

JMIR Aging
|April 13, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) significantly reduced decisional conflict and anxiety for older men undergoing prostate-specific antigen (PSA) screening. This AI-driven tool enhanced decision satisfaction and confidence, supporting patient-centered care in aging populations.

Keywords:
PSA screeningdecision aidsdecisional conflictmachine learningpatient-centered carepersonalized recommendationsprostate cancerprostate-specific antigenshared decision-making

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Area of Science:

  • Geriatric Medicine
  • Artificial Intelligence in Healthcare
  • Shared Decision-Making

Background:

  • Prostate-specific antigen (PSA) screening presents complex trade-offs between early detection and overdiagnosis risks.
  • Shared decision-making (SDM) for older adults is challenged by health literacy, impairments, and multimorbidity.
  • Traditional decision aids lack personalization, while machine learning (ML) effects on geriatric SDM are understudied.

Purpose of the Study:

  • To develop and evaluate a web-based, ML-driven decision aid for personalized PSA screening recommendations.
  • To assess the impact of ML-assisted SDM on decisional conflict, anxiety, and decision satisfaction in middle-aged and older men.

Main Methods:

  • A 2-stage design: model establishment (n=507) using ML algorithms, followed by a randomized controlled trial (n=367).
  • A random forest model demonstrated superior performance (AUC 0.933).
  • Participants were randomized to an ML suggestion group (MLSG) or control group (CG), receiving standard care plus an ML recommendation in the MLSG.

Main Results:

  • The MLSG reported significantly lower decisional conflict (MD -3.77, P<.001) and higher decision satisfaction (MD -7.38, P<.001).
  • MLSG showed reduced worry, increased calmness, greater perceived support, and more adequate advice.
  • ML recommendations influenced behavioral choices, with higher 'accept' rates when suggested by ML.

Conclusions:

  • Personalized ML recommendations in SDM provide emotional support, reducing distress and enhancing confidence for older men.
  • This ML-driven approach addresses geriatric vulnerabilities via a digital interface, complementing clinical consultations.
  • Findings support scalable integration of AI-assisted decision support for patient-centered care in aging populations.