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Digital twin monitoring system for chronic disease patients: a simulation-based proof-of-concept

  • Bushra Abbas
    ,
  • Saif Ur Rehman Malik
    ,
  • Munam Ali Shah
    ,
  • Majid Iqbal Khan
    ,
  • Gautam Srivastava
    ,
  • Syed Atif Moqurrab
Research Output: Contribution to journal Article Peer-review

Open access

Sustainable Development Goals

  • SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well

Abstract

The applications of Digital Twin (DT) technology in healthcare have shownpotential to transform patient care. This paper presents a simulation-basedproof-of-concept Patient Digital Twin (PDT) for monitoring chronic diseases,namely hypertension, diabetes, and respiratory conditions, utilizing publiclyavailable datasets to demonstrate the potential for remote patient support.The PDT can simulate remote monitoring of a patient's health status byintegrating physiological data streams and medical records. The PDT frame-work was demonstrated through simulation-based experiments using publiclyavailable datasets, enabling proof-of-concept evaluation of patient monitoringand drug-class prediction pipeline. However, the existing PDTs for patientremote monitoring need to be shaped with suffcient information to providea comprehensive analysis. This limitation can be addressed by incorporatingthe patient's medical records along with real-time vitals in the PDT, allow-ing the patient's health status to be monitored and analyzed more effectively.Through it, the patient's health status can be monitored and analyzed ef-fectively. We designed and simulated a PDT for Chronic Disease Patients(CDP) in a simulation-based setting to demonstrate the concept. We emu-lated the collection of Patient Vitals (PV) using public datasets to representsensors attached to the patient's body (Physical Twin, PT) and accessed sim-ulated medical records. By real-time simulating the patient data, a DT of thepatient (i.e., virtual replica) is created. We performed cloud-based real-timesimulation so that DT can be accessed remotely and continuously observedin a simulated environment to demonstrate the feasibility of early abnor-mality detection and timely intervention. The drug-class prediction moduleachieved 96.53% accuracy on a rule-driven synthetic dataset, demonstratingthe effectiveness of the system pipeline as a preliminary validation step beforeproceeding to clinical evaluation

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Article number

0103

Journal (Volume, Issue Number)

Computational and Structural Biotechnology Journal

Publication milestones

  • E-pub ahead of print - 08/05/2026
  • Published - 08/05/2026

Publication status

Published - 08/05/2026

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