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Constructing and Visualizing Models using Mime-based Machine-learning Framework
Published on: July 22, 2025
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Artificial intelligence with multi-functional machine learning platform development for better healthcare and
Zeeshan Ahmed1,2,3,4, Khalid Mohamed3, Saman Zeeshan5
1Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, 112 Paterson Street, New Brunswick, NJ, USA.
Database : the Journal of Biological Databases and Curation
|March 19, 2020
Summary
Precision medicine uses artificial intelligence (AI) and machine learning (ML) to analyze patient data for personalized treatments. This approach enhances early intervention and improves health outcomes by integrating electronic health records and data systems.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Computational Biology
Background:
- Precision medicine offers a paradigm shift from traditional symptom-driven care to personalized interventions.
- Analyzing comprehensive patient data is crucial for distinguishing health states and understanding disease indicators.
- Technological advancements are overcoming previous limitations in utilizing healthcare information for clinical decisions.
Purpose of the Study:
- To review and discuss artificial intelligence (AI) and machine learning (ML) solutions for advancing precision medicine.
- To explore approaches for integrating disparate data sources, including electronic health records, for patient-specific pattern discovery.
- To highlight the need for analytical tools, robust databases, and ethical frameworks to support data-centric healthcare.
Main Methods:
- Systematic review and analysis of published AI and ML solutions in precision medicine.
- Discussion of approaches for data extraction, aggregation, management, and analysis from clinical and public health systems.
- Examination of strategies to enhance networking, interoperability, and address ethical considerations in healthcare data management.
Main Results:
- AI and ML platforms can significantly improve clinical decision-making through efficient subject stratification and pattern recognition.
- Integration of electronic health records and disparate data sources is key to unlocking personalized and population medicine insights.
- Multifunctional machine learning platforms are essential for real-time decision support and optimizing patient outcomes.
Conclusions:
- The implementation of AI and ML in healthcare promises significant improvements in personalized and population medicine.
- A data-centric approach, supported by advanced analytics and interoperable systems, is paving the way for a new era of medical discovery.
- Balancing data utilization with ethical considerations, including privacy and protection, is vital for the successful advancement of precision medicine.
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