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Related Experiment Video

Updated: May 19, 2026

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

Decision support system based semantic web for personalized patient care.

Nassim Douali¹, Jos De Roo, Marie-Christine Jaulent

¹Pierre and Marie Curie University, Paris, France. nassim.douali@crc.jussieu.fr

Studies in Health Technology and Informatics

|August 10, 2012

Summary

This summary is machine-generated.

This study introduces a decision support system for personalized medicine, leveraging patient genetic profiles to guide treatment selection. The system aims to enhance healthcare quality, safety, and efficiency through precise, individualized patient care.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Published on: June 13, 2025

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Last Updated: May 19, 2026

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Biomedical Informatics
Genomics
Clinical Decision Support

Background:

Personalized medicine offers enhanced precision over traditional disease treatment.
Patient genetic variation profiles are crucial for optimizing drug selection and treatment protocols.
Minimizing adverse effects and improving treatment success rates are key goals.

Purpose of the Study:

To describe a decision support system (DSS) for personalized medicine.
To outline a methodology for integrating the DSS into clinical workflows.
To advance the development of semantic web-based clinical decision support for personalized patient care.

Main Methods:

Development of a decision support system tailored for personalized care.
Implementation of a reasoning method to integrate heterogeneous knowledge and data.
Focus on semantic web technologies for healthcare applications.

Main Results:

The described system assists physicians in providing personalized care.
Methodology for seamless integration into clinical workflows is presented.
The approach facilitates the interaction of diverse knowledge and data sources.

Conclusions:

Clinical decision support systems based on semantic web are vital for realizing the potential of personalized medicine.
Such systems can significantly improve the quality, safety, and efficiency of healthcare delivery.
Precision in patient care through genetic profiling and advanced decision support is the future of medicine.