Hybrid modelling using simulation and machine learning in healthcare

Abstract

Modelling & Simulation (M&S) and Machine Learning (ML) methodologies have undergone significant advancements, enabling transformative applications across various industries. The integration of M&S and ML into a Hybrid M&S-ML approach leverages the unique strengths of both fields, offering enhanced model precision, improved efficiency, and more effective decision support. This review explores the increasing convergence of ML algorithms with traditional M&S methods- namely Agent-Based Modelling & Simulation, Discrete Event Simulation, and System Dynamics- in healthcare applications. Through a systematic review of 90 relevant studies, this article provides a comprehensive synthesis of the current state-of-the-art Hybrid M&S-ML in healthcare. Specifically, it examines the M&S and ML methodologies employed, associated software tools and programming languages, analyses integration patterns and data exchange mechanisms, and explores application domains, as well as the types and motivations for hybridisation. Key findings highlight prominent methodological and technical trends, as well as opportunities for combining M&S with ML to address healthcare challenges. These insights provide direction for modellers and data scientists in developing hybrid M&S–ML approaches that more effectively combine simulation capabilities with data-driven learning. The review also demonstrates the potential of such approaches to advance methodological innovation and support evidence-based decision-making in diverse healthcare contexts.