Enabling Cognitive Digital Twins in Healthcare: Data Types and Application Insights from Machine Learning Research

Abstract: Cognitive Digital Twins (CDTs) emerge as an evolution of the Digital Twin (DT) concept, incorporating cognitive capabilities to enhance data-driven decision-making in complex and dynamic systems such as healthcare. The integration of machine learning (ML) promises transformative approach to healthcare facility management (FM). This study explores key data types and application areas relevant to CDT development, derived from a review of ML implementations in healthcare. The findings categorize data types, including health records, and appointment data, alongside application areas such as decision support and waiting time prediction. These insights provide a novel foundation for CDT-driven healthcare solutions, enabling proactive management.