Alzheimer's disease is a progressive, debilitating neurodegenerative disease that affects 50 million people globally. Despite this substantial health burden, available treatments for the disease are limited and its fundamental causes remain poorly understood. Previous work has suggested the existence of clinically-meaningful sub-types, which it is suggested may correspond to distinct etiologies, disease courses, and ultimately appropriate treatments. Here, we use unsupervised learning techniques on electronic health records (EHRs) from a cohort of memory disorder patients to characterise heterogeneity in this disease population. Pre-trained embeddings for medical codes as well as transformer-derived Clinical BERT embeddings of free text are used to encode patient EHRs. We identify the existence of sub-populations on the basis of comorbidities and shared textual features, and discuss their clinical significance.

阿尔茨海默病是一种进展性的神经退行性疾病，全球有5000万人患病。尽管这是一种重大的健康负担，可用于治疗该疾病的方法有限，并且其根本原因仍然不明。我们使用无监督学习技术对来自一组记忆障碍患者的电子健康记录进行特征化分析，以揭示该疾病人群的异质性。通过对医疗代码的预训练嵌入以及基于Transformer的临床BERT嵌入对患者电子健康记录进行编码，我们确认了亚群体的存在，并讨论了它们的临床意义。

利用预训练和基于Transformer的EHR嵌入来表征阿尔茨海默氏病及相关痴呆症之间的异质性