Bridging paradigms: hybrid mechanistic-discriminative predictive models

Area of Science:

• Computational biology
• Medical informatics
• Artificial intelligence in healthcare

Background:

• Complex diseases involve multiple simultaneous stochastic processes.
• Current analytical methods often treat mechanistic models and machine learning (ML) as separate approaches.
• Bridging these viewpoints is crucial for advancing personalized medicine.

Purpose of the Study:

• To review the application of mechanistic models in healthcare.
• To examine the utilization of machine learning (ML) in medical contexts.
• To highlight emerging hybrid methods integrating both approaches for advanced intelligent systems.

Main Methods:

• Literature review of mechanistic modeling in healthcare.
• Analysis of machine learning applications in medicine.
• Synthesis of research on hybrid mechanistic-ML approaches.

Main Results:

• Mechanistic models provide biological insights, while ML excels at data-driven pattern recognition.
• Existing approaches are often siloed, limiting comprehensive analysis.
• Hybrid methods demonstrate potential for creating biologically informed, data-driven intelligent systems.

Conclusions:

• Integrating mechanistic models and ML offers a powerful paradigm for complex disease analysis.
• Hybrid approaches are key to developing sophisticated, personalized medical solutions.
• Future research should focus on refining these integrated methods for clinical translation.