Integrative computational modelling of endocrine dysregulation and post-hysterectomy healing mechanisms in gynaecological disorders.

Authors

Huaqing Li, Yanqiu Deng, Yanhua Wei, Dandan Tu

Published in

Archives of physiology and biochemistry. Pages 1-26. Nov 11, 2026. Epub Nov 11, 2026.

Abstract

The interplay between endocrine imbalance and post-hysterectomy healing in gynaecological disorders remains insufficiently understood due to their conventional evaluation as independent processes.
This study presents ICMED-HG, an integrative computational framework that combines multi-modal data, including molecular profiles, clinical parameters, and ultrasonographic measurements, using machine learning and systems biology approaches.
The model captures the dynamic interactions between hormonal dysregulation and tissue regeneration, enabling improved prediction of disease progression and recovery outcomes.
Compared with conventional models, ICMED-HG achieves a 15% increase in diagnostic accuracy, along with improvements of 18% in sensitivity and 12% in specificity. Additionally, it identifies key biomarkers and delineates personalised healing pathways, thereby enhancing prognostic precision and supporting clinical decision-making.
Overall, the proposed framework bridges the gap between molecular mechanisms and clinical outcomes, contributing to the advancement of precision medicine in gynaecology and enabling more effective, patient-specific therapeutic strategies.

PMID:
43013635
Bibliographic data and abstract were imported from PubMed on 12 Nov 2026.

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