Human Digital Twin, Precision Healthcare, Health Monitoring, Anomaly Detection, Statistical and Machine Learning Models

Rong Pan; Hongyue Sun; Xiaoyu Chen; Giulia Pedrielli; Jiapeng Huang

doi:10.1109/ICDMW69685.2025.00195

Human Digital Twin, Precision Healthcare, Health Monitoring, Anomaly Detection, Statistical and Machine Learning Models

Rong Pan
, Hongyue Sun
, Xiaoyu Chen
, Giulia Pedrielli
, Jiapeng Huang

Department of Industrial and Systems Engineering

Arizona State University
University of Louisville

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

Abstract

Human digital twins (HDTs) are dynamic, datadriven virtual representations of individuals, continuously updated with multimodal data to simulate, monitor, and predict health trajectories. By integrating clinical, physiological, behavioral, and environmental inputs, HDTs enable personalized diagnostics, treatment planning, and anomaly detection. This paper reviews current approaches to HDT modeling, with a focus on statistical and machine learning techniques, including recent advances in anomaly detection and failure prediction. It also discusses data integration, computational methods, and ethical, technological, and regulatory challenges in deploying HDTs for precision healthcare.

Original language	English
Title of host publication	Proceedings - 25th IEEE International Conference on Data Mining Workshops, ICDMW 2025
Publisher	IEEE Computer Society
Pages	1624-1630
Number of pages	7
ISBN (Electronic)	9798331581329
DOIs	https://doi.org/10.1109/ICDMW69685.2025.00195
State	Published - 2025
Event	25th IEEE International Conference on Data Mining Workshops, ICDMW 2025 - Washington, United States Duration: Nov 12 2025 → Nov 15 2025

Publication series

Name	IEEE International Conference on Data Mining Workshops, ICDMW
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Conference

Conference	25th IEEE International Conference on Data Mining Workshops, ICDMW 2025
Country/Territory	United States
City	Washington
Period	11/12/25 → 11/15/25

Keywords

Anomaly Detection
Health Monitoring
Human Digital Twin
Precision Healthcare
Statistical and Machine Learning Models

Access to Document

10.1109/ICDMW69685.2025.00195

Cite this

Pan, R., Sun, H., Chen, X., Pedrielli, G., & Huang, J. (2025). Human Digital Twin, Precision Healthcare, Health Monitoring, Anomaly Detection, Statistical and Machine Learning Models. In Proceedings - 25th IEEE International Conference on Data Mining Workshops, ICDMW 2025 (pp. 1624-1630). (IEEE International Conference on Data Mining Workshops, ICDMW). IEEE Computer Society. https://doi.org/10.1109/ICDMW69685.2025.00195

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title = "Human Digital Twin, Precision Healthcare, Health Monitoring, Anomaly Detection, Statistical and Machine Learning Models",

abstract = "Human digital twins (HDTs) are dynamic, datadriven virtual representations of individuals, continuously updated with multimodal data to simulate, monitor, and predict health trajectories. By integrating clinical, physiological, behavioral, and environmental inputs, HDTs enable personalized diagnostics, treatment planning, and anomaly detection. This paper reviews current approaches to HDT modeling, with a focus on statistical and machine learning techniques, including recent advances in anomaly detection and failure prediction. It also discusses data integration, computational methods, and ethical, technological, and regulatory challenges in deploying HDTs for precision healthcare.",

keywords = "Anomaly Detection, Health Monitoring, Human Digital Twin, Precision Healthcare, Statistical and Machine Learning Models",

author = "Rong Pan and Hongyue Sun and Xiaoyu Chen and Giulia Pedrielli and Jiapeng Huang",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 25th IEEE International Conference on Data Mining Workshops, ICDMW 2025 ; Conference date: 12-11-2025 Through 15-11-2025",

year = "2025",

doi = "10.1109/ICDMW69685.2025.00195",

language = "English",

series = "IEEE International Conference on Data Mining Workshops, ICDMW",

publisher = "IEEE Computer Society",

pages = "1624--1630",

booktitle = "Proceedings - 25th IEEE International Conference on Data Mining Workshops, ICDMW 2025",

address = "United States",

}

Pan, R, Sun, H, Chen, X, Pedrielli, G & Huang, J 2025, Human Digital Twin, Precision Healthcare, Health Monitoring, Anomaly Detection, Statistical and Machine Learning Models. in Proceedings - 25th IEEE International Conference on Data Mining Workshops, ICDMW 2025. IEEE International Conference on Data Mining Workshops, ICDMW, IEEE Computer Society, pp. 1624-1630, 25th IEEE International Conference on Data Mining Workshops, ICDMW 2025, Washington, United States, 11/12/25. https://doi.org/10.1109/ICDMW69685.2025.00195

Human Digital Twin, Precision Healthcare, Health Monitoring, Anomaly Detection, Statistical and Machine Learning Models. / Pan, Rong; Sun, Hongyue; Chen, Xiaoyu et al.
Proceedings - 25th IEEE International Conference on Data Mining Workshops, ICDMW 2025. IEEE Computer Society, 2025. p. 1624-1630 (IEEE International Conference on Data Mining Workshops, ICDMW).

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

TY - GEN

T1 - Human Digital Twin, Precision Healthcare, Health Monitoring, Anomaly Detection, Statistical and Machine Learning Models

AU - Pan, Rong

AU - Sun, Hongyue

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AU - Pedrielli, Giulia

AU - Huang, Jiapeng

PY - 2025

Y1 - 2025

N2 - Human digital twins (HDTs) are dynamic, datadriven virtual representations of individuals, continuously updated with multimodal data to simulate, monitor, and predict health trajectories. By integrating clinical, physiological, behavioral, and environmental inputs, HDTs enable personalized diagnostics, treatment planning, and anomaly detection. This paper reviews current approaches to HDT modeling, with a focus on statistical and machine learning techniques, including recent advances in anomaly detection and failure prediction. It also discusses data integration, computational methods, and ethical, technological, and regulatory challenges in deploying HDTs for precision healthcare.

AB - Human digital twins (HDTs) are dynamic, datadriven virtual representations of individuals, continuously updated with multimodal data to simulate, monitor, and predict health trajectories. By integrating clinical, physiological, behavioral, and environmental inputs, HDTs enable personalized diagnostics, treatment planning, and anomaly detection. This paper reviews current approaches to HDT modeling, with a focus on statistical and machine learning techniques, including recent advances in anomaly detection and failure prediction. It also discusses data integration, computational methods, and ethical, technological, and regulatory challenges in deploying HDTs for precision healthcare.

KW - Anomaly Detection

KW - Health Monitoring

KW - Human Digital Twin

KW - Precision Healthcare

KW - Statistical and Machine Learning Models

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U2 - 10.1109/ICDMW69685.2025.00195

DO - 10.1109/ICDMW69685.2025.00195

M3 - Conference contribution

AN - SCOPUS:105035383263

T3 - IEEE International Conference on Data Mining Workshops, ICDMW

SP - 1624

EP - 1630

BT - Proceedings - 25th IEEE International Conference on Data Mining Workshops, ICDMW 2025

PB - IEEE Computer Society

T2 - 25th IEEE International Conference on Data Mining Workshops, ICDMW 2025

Y2 - 12 November 2025 through 15 November 2025

ER -

Pan R, Sun H, Chen X, Pedrielli G, Huang J. Human Digital Twin, Precision Healthcare, Health Monitoring, Anomaly Detection, Statistical and Machine Learning Models. In Proceedings - 25th IEEE International Conference on Data Mining Workshops, ICDMW 2025. IEEE Computer Society. 2025. p. 1624-1630. (IEEE International Conference on Data Mining Workshops, ICDMW). doi: 10.1109/ICDMW69685.2025.00195

Human Digital Twin, Precision Healthcare, Health Monitoring, Anomaly Detection, Statistical and Machine Learning Models

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Keywords

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