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

Tugçe Baçnak; Yusuf Arayici

doi:10.35490/EC3.2025.333

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

Tugçe Baçnak^*, Yusuf Arayici

^*Corresponding author for this work

School of Architecture and Built Environment

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings of the 2025 European Conference on Computing in Construction
Place of Publication	Porto, Portugal
Publisher	European Council on Computing in Construction (EC3)
Number of pages	8
Volume	6
ISBN (Print)	9789083451312
DOIs	https://doi.org/10.35490/EC3.2025.333
Publication status	Published - 14 Jul 2025

Access to Document

10.35490/EC3.2025.333Licence: CC BY-NC-ND

EC32025_333Final published version, 0.98 MBLicence: CC BY-NC-ND

Cite this

@inproceedings{7aec7fcfd3cf4956afe4dd7c7d092851,

title = "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.",

author = "Tug{\c c}e Ba{\c c}nak and Yusuf Arayici",

year = "2025",

month = jul,

day = "14",

doi = "10.35490/EC3.2025.333",

language = "English",

isbn = "9789083451312",

volume = "6",

booktitle = "Proceedings of the 2025 European Conference on Computing in Construction",

publisher = "European Council on Computing in Construction (EC3)",

}

Enabling Cognitive Digital Twins in Healthcare: Data Types and Application Insights from Machine Learning Research. / Baçnak, Tugçe ; Arayici, Yusuf.
Proceedings of the 2025 European Conference on Computing in Construction. Vol. 6 Porto, Portugal: European Council on Computing in Construction (EC3), 2025.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Enabling Cognitive Digital Twins in Healthcare: Data Types and Application Insights from Machine Learning Research

AU - Baçnak, Tugçe

AU - Arayici, Yusuf

PY - 2025/7/14

Y1 - 2025/7/14

N2 - 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.

AB - 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.

U2 - 10.35490/EC3.2025.333

DO - 10.35490/EC3.2025.333

M3 - Conference contribution

SN - 9789083451312

VL - 6

BT - Proceedings of the 2025 European Conference on Computing in Construction

PB - European Council on Computing in Construction (EC3)

CY - Porto, Portugal

ER -