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Hybrid modelling using simulation and machine learning in healthcare.

Ali Ahmadi1, Masoud Fakhimi1, Carin Magnusson2

  • 1Surrey Business School, University of Surrey, Guildford, UK.

Computers & Operations Research
|November 5, 2025
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Summary
This summary is machine-generated.

Hybrid Modelling & Simulation (M&S) and Machine Learning (ML) approaches enhance healthcare by combining simulation with data-driven learning. This review synthesizes trends and opportunities for M&S-ML in healthcare decision-making.

Keywords:
Data scienceHealthcareHybrid modellingMachine learningModelling and simulation

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Area of Science:

  • Healthcare Informatics
  • Computational Science
  • Artificial Intelligence

Background:

  • Modelling & Simulation (M&S) and Machine Learning (ML) are advancing rapidly.
  • Integrating M&S and ML into Hybrid M&S-ML offers improved precision, efficiency, and decision support.
  • Healthcare applications increasingly benefit from these combined methodologies.

Purpose of the Study:

  • To review the state-of-the-art Hybrid M&S-ML in healthcare.
  • To synthesize methodologies, tools, integration patterns, and applications.
  • To identify trends and opportunities for M&S-ML in addressing healthcare challenges.

Main Methods:

  • Systematic review of 90 relevant studies.
  • Analysis of M&S and ML methodologies used in hybrid approaches.
  • Examination of software, programming languages, integration patterns, and data exchange.
  • Categorization of application domains, types, and motivations for hybridisation.

Main Results:

  • Identified prominent methodological and technical trends in Hybrid M&S-ML for healthcare.
  • Highlighted opportunities for combining M&S with ML to solve healthcare problems.
  • Synthesized current practices and future directions for M&S-ML integration.

Conclusions:

  • Hybrid M&S-ML approaches show significant potential for advancing healthcare.
  • These methods can improve methodological innovation and support evidence-based decision-making.
  • Further development of M&S-ML is crucial for addressing complex healthcare challenges.