Causal Bio-Semantic and Multi-Scale Graph Intelligence Framework for Early Detection of Gynaecological Disorders in PCOS Cohorts

Abstract

The current challenge with identifying Gynecologic Disorders (such as PCOS) at an early stage is that clinicians do not have a clear understanding of how the many different symptoms associated with these disorders interact through multiple pathways of the body's endocrine system; therefore, when using traditional analytical methods to understand this interaction, there is little structure to identify relationships. As a result, most Machine Learning models that have been used for this purpose rely solely upon correlation between static features in the data being analyzed and do not provide the ability for causal analysis or the ability to generalize results from one population of patients to another. Therefore, these models are limited in their ability to be reliable for diagnostic purposes. To fill these gaps, we propose to create an Analytical Framework that incorporates six modules of which include: Bio-Semantic Feature Harmonization Network, Causal Endocrine Interaction Mapper, Multi-Scale Hormonal Pattern Extractor Network, Adaptive Risk Stratification Graph Transformer, Federated Clinical Validation and Drift Analyzer, and Explainable Clinical Decision Fusion Engine. These modules will integrate clinical data and transform it into semantically rich tensor representations, map causal hormonal dependencies within the endocrine system, extract multi-scale latent patterns of hormone activity and perform patient specific risk assessment utilizing federated robustness and explainable inference. Our proposed model demonstrates a predictive accuracy of 96.3%, provides significant enhancements to both interpretability and cross domain stability and therefore can enhance early diagnosis and support clinically informed decision making.