We introduce a novel profile-based patient clustering model designed for
clinical data in healthcare. By utilizing a method grounded on constrained
low-rank approximation, our model takes advantage of patients' clinical data
and digital interaction data, including browsing and search, to construct
patient profiles. As a result of the method, nonnegative embedding vectors are
generated, serving as a low-dimensional representation of the patients. Our
model was assessed using real-world patient data from a healthcare web portal,
with a comprehensive evaluation approach which considered clustering and
recommendation capabilities. In comparison to other baselines, our approach
demonstrated superior performance in terms of clustering coherence and
recommendation accuracy.

本研究介绍了一种基于患者数据的新型患者聚类模型，利用约束低秩逼近的方法，结合患者的临床数据以及数字交互数据（包括浏览和搜索），构建患者个人资料。通过生成非负嵌入向量作为患者低维表示，我们的模型在真实世界的患者数据上进行了评估，综合考虑了聚类和推荐功能，与其他对照组相比，我们的方法在聚类一致性和推荐准确性方面表现出卓越性能。