Innovative Application of Knowledge Graph-Driven Causal Inference in Digital Twin of Chronic Disease Progression

Jia Wang; Lin Li

doi:10.54254/2753-8818/2025.25630

Theoretical and Natural ScienceOpen access

Innovative Application of Knowledge Graph-Driven Causal Inference in Digital Twin of Chronic Disease Progression

Research Article

Open Access

Innovative Application of Knowledge Graph-Driven Causal Inference in Digital Twin of Chronic Disease Progression

Jia Wang ^1* Lin Li ²

¹ University of Reading, Reading, UK

² Ocean University of China, Shandong, China

^*Corresponding author: rara481846778@gmail.com

Published on 30 July 2025

TNS Vol.134

ISSN (Print): 2753-8826

ISSN (Online): 2753-8818

ISBN (Print): 978-1-80590-307-9

ISBN (Online): 978-1-80590-308-6

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Abstract

A patient-centric digital-twin architecture that fuses ontology-grounded knowledge graphs with structural causal inference is presented to simulate the five-year evolution of type 2 diabetes mellitus and cardio-renal comorbidities. A harmonised health-information-exchange corpus comprising 12 318 adults, 22.7 million encounter rows and 7.4 million laboratory records (2010 – 2024) was mapped to a 168 402-node, 1 217 965-edge graph aligned to SNOMED-CT. Counterfactual trajectories under 17 therapeutic bundles were generated by a differentiable do-calculus engine nested inside a temporal graph transformer, producing 1 000 Monte-Carlo roll-outs per patient. External validation on an independent 2 975-subject cohort yielded a dynamic concordance index of 0.842, an integrated Brier score of 0.091 and a calibration-in-the-large of –0.013, surpassing recurrent neural and mechanistic baselines by 18.5 % and 11.2 % respectively. Sensitivity analyses confirmed robustness to 24 % MCAR missingness and ±15 % hidden-confounding bias. The findings demonstrate that knowledge-graph-driven causal twins deliver granular, well-calibrated forecasts and quantitatively rank preventive strategies, paving the way for learning-health-system deployment in chronic-disease management.

Keywords:

digital twin, knowledge graph, causal inference, type 2 diabetes, chronic-disease simulation

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References

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References

[3]. Mosquera-Lopez, Clara, and Peter G. Jacobs. "Digital twins and artificial intelligence in metabolic disease research." Trends in Endocrinology & Metabolism (2024).

[5]. Raudenbush, Stephen W. "Comparing personal trajectories and drawing causal inferences from longitudinal data." Annual review of psychology 52.1 (2001): 501-525.

[8]. Coorey, Genevieve, et al. "The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field." NPJ digital medicine 5.1 (2022): 126.

[9]. Barbiero, Pietro, Ramon Vinas Torne, and Pietro Lió. "Graph representation forecasting of patient's medical conditions: toward a digital twin." Frontiers in genetics 12 (2021): 652907.

Cite this article

Wang,J.;Li,L. (2025). Innovative Application of Knowledge Graph-Driven Causal Inference in Digital Twin of Chronic Disease Progression. Theoretical and Natural Science,134,8-13.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: The 3rd International Conference on Applied Physics and Mathematical Modeling

ISBN: 978-1-80590-307-9(Print) / 978-1-80590-308-6(Online)

Editor: Marwan Omar

Conference website: https://2025.confapmm.org/

Conference date: 31 October 2025

Series: Theoretical and Natural Science

Volume number: Vol.134

ISSN: 2753-8818(Print) / 2753-8826(Online)

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