为渐冻症患者设计可解释的聚类距离，提升个性化诊疗研究效率。
Learning a Distance for the Clustering of Patients with Amyotrophic Lateral Sclerosis
- 基于疾病进展评分与多变量融合，学习适用于患者序列的聚类距离
- 在353名患者数据上，生存分析表现优于现有方法，轮廓系数相当
- 结果更契合临床专家判断，适合医疗科研与精准医学场景
肌萎缩侧索硬化症（ALS）是一种严重疾病，通常症状出现后生存期为3-5年。现有治疗手段仅能有限延长寿命，患者反应差异大，亟需个性化诊疗。然而，小规模异质队列、稀疏纵向数据及缺乏临床有意义的患者分群定义，制约了研究进展。现有聚类方法在广度与数量上均受限。为此，本文提出一种基于疾病进展声明式评分的聚类方法，整合医学知识，通过多个描述性变量构建多种距离度量，既复用现成距离，也采用弱监督学习方法。将这些距离与聚类算法结合，并在353名来自图尔大学医院的患者数据集上进行评估。结果表明，该方法在生存分析中优于当前最先进方法，轮廓系数相当；同时所学距离显著提升结果的临床相关性与可解释性。
原文摘要 · Abstract (English)
Amyotrophic lateral sclerosis (ALS) is a severe disease with a typical survival of 3-5 years after symptom onset. Current treatments offer only limited life extension, and the variability in patient responses highlights the need for personalized care. However, research is hindered by small, heterogeneous cohorts, sparse longitudinal data, and the lack of a clear definition for clinically meaningful patient clusters. Existing clustering methods remain limited in both scope and number. To address this, we propose a clustering approach that groups sequences using a disease progression declarative score. Our approach integrates medical expertise through multiple descriptive variables, investigating several distance measures combining such variables, both by reusing off-the-shelf distances and employing a weak-supervised learning method. We pair these distances with clustering methods and benchmark them against state-of-the-art techniques. The evaluation of our approach on a dataset of 353 ALS patients from the University Hospital of Tours, shows that our method outperforms state-of-the-art methods in survival analysis while achieving comparable silhouette scores. In addition, the learned distances enhance the relevance and interpretability of results for medical experts.
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