通过可解释聚类分析，识别睡眠障碍患者的关键特征分组。
User Profiles of Sleep Disorder Sufferers: Towards Explainable Clustering and Differential Variable Analysis
- 基于聚类方法对患者进行分型，结合可解释AI识别关键影响因素。
- 在匿名真实数据上验证了分型的有效性与临床相关性。
- 适用于临床辅助诊断和个性化治疗方案设计。
睡眠障碍对患者健康和生活质量有重大影响，但其诊断因症状多样性而复杂。借助技术进步与医疗数据分析，为深入理解这些障碍提供了新视角。特别是可解释人工智能（XAI）旨在使AI模型决策对用户透明可理解。本研究提出一种基于聚类的方法，根据不同的睡眠障碍特征对患者进行分组。通过整合可解释性方法，识别出影响这些病理的关键因素。在匿名化真实数据上的实验验证了该方法的有效性和相关性。
原文摘要 · Abstract (English)
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.
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