What question did this study set out to answer?

The central aim is to explore how machine learning can enhance personalized medication regimens for older adults by improving pharmacokinetic and pharmacodynamic models.

April 23, 2026TechnologiesOpen Access

Machine Learning in Personalized Medication Regimen Design for the Geriatric Population: Integrating Pharmacokinetic and Pharmacodynamic Modeling with Clinical Decision-Making

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Authors

AAAhmad R. Alsayed MAMohanad Al-DarrajiApplied Science Private University MAMohannad Al-QaiseiahAl-Zaytoonah University of Jordan

Discussion

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Implication

This review examines integrating machine learning with pharmacokinetic and pharmacodynamic modeling to enhance medication dosing accuracy in the geriatric population, suggesting improved clinical...

Key Points

The central aim is to explore how machine learning can enhance personalized medication regimens for older adults by improving pharmacokinetic and pharmacodynamic models.
Reviewed integration of machine learning techniques with pharmacokinetic and pharmacodynamic models.
Evaluated methods like Random Forest and XGBoost for accuracy in dosing predictions.
Discussed the importance of explainability tools and FAIR data principles for trust in models.
Machine learning models demonstrated superior predictive accuracy compared to conventional methods.
Random Forest and XGBoost provided more efficient computational workflows with better exposure predictions.
Concerns regarding algorithmic bias and the need for transparency in model outputs were highlighted.

Cite This Study

Alsayed et al. (2026) studied this question.

synapsesocial.com/papers/69e9bb6285696592c86ed1a3 https://doi.org/10.3390/technologies14040241

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April 23, 2026TechnologiesOpen Access

Machine Learning in Personalized Medication Regimen Design for the Geriatric Population: Integrating Pharmacokinetic and Pharmacodynamic Modeling with Clinical Decision-Making

View Full Paper

Ask AI

Bookmark

Authors

AAAhmad R. Alsayed MAMohanad Al-DarrajiApplied Science Private University MAMohannad Al-QaiseiahAl-Zaytoonah University of Jordan

Discussion

Member takes

Implication

This review examines integrating machine learning with pharmacokinetic and pharmacodynamic modeling to enhance medication dosing accuracy in the geriatric population, suggesting improved clinical...

Key Points

The central aim is to explore how machine learning can enhance personalized medication regimens for older adults by improving pharmacokinetic and pharmacodynamic models.
Reviewed integration of machine learning techniques with pharmacokinetic and pharmacodynamic models.
Evaluated methods like Random Forest and XGBoost for accuracy in dosing predictions.
Discussed the importance of explainability tools and FAIR data principles for trust in models.
Machine learning models demonstrated superior predictive accuracy compared to conventional methods.
Random Forest and XGBoost provided more efficient computational workflows with better exposure predictions.
Concerns regarding algorithmic bias and the need for transparency in model outputs were highlighted.

Cite This Study

Alsayed et al. (2026) studied this question.

synapsesocial.com/papers/69e9bb6285696592c86ed1a3 https://doi.org/10.3390/technologies14040241

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