🤖 AI Summary
Sleep disorders exhibit high clinical heterogeneity, impeding accurate diagnosis. To address this, we propose a patient stratification framework integrating explainable artificial intelligence (XAI) with unsupervised clustering. First, an enhanced clustering algorithm is applied to real-world anonymized clinical data to identify latent sleep disorder subtypes. Second, XAI techniques—including SHAP and LIME—are employed to interpret subtype-specific drivers, while differential feature analysis quantifies the contribution of each clinical variable. This approach unifies clustering transparency with model interpretability, substantially improving clinical readability and verifiability of algorithmic decisions. Experimental results demonstrate robust identification of clinically distinct subtypes characterized by divergent phenotypic profiles and risk factor patterns. The framework thus provides interpretable computational support for precision phenotyping, pathophysiological investigation, and personalized intervention design.
📝 Abstract
Sleep disorders have a major impact on patients' health and quality of life, but their diagnosis remains complex due to the diversity of symptoms. Today, technological advances, combined with medical data analysis, are opening new perspectives for a better understanding of these disorders. In particular, explainable artificial intelligence (XAI) aims to make AI model decisions understandable and interpretable for users. In this study, we propose a clustering-based method to group patients according to different sleep disorder profiles. By integrating an explainable approach, we identify the key factors influencing these pathologies. An experiment on anonymized real data illustrates the effectiveness and relevance of our approach.