WellFactor：利用医疗数据整合嵌入进行患者画像分析
WellFactor: Patient Profiling using Integrative Embedding of Healthcare Data
AI总结：
WellFactor 通过约束低秩逼近整合多源医疗数据生成患者画像，能即时计算新患者嵌入，并在分类、聚类和相似性搜索中优于现有方法。
摘要：
在快速发展的医疗行业中，平台如今不仅能够访问传统的医疗记录，还能获取涵盖各种患者互动的多样化数据集，例如来自医疗门户网站的数据。为了应对这种丰富的数据多样性，我们提出了 WellFactor：一种通过整合这些来源的信息来生成患者画像的方法。我们方法的核心在于利用约束低秩逼近。WellFactor 经过优化，能够处理医疗数据中通常固有的稀疏性问题。此外，通过融入任务特定的标签信息，我们的方法细化了嵌入结果，为患者提供了更具信息量的视角。WellFactor 的一个重要特点是，它能够即时计算新的、先前未观察到的患者数据的嵌入，无需重新访问整个数据集或重新计算嵌入。在真实世界医疗数据上的全面评估证明了 WellFactor 的有效性。与其他现有方法相比，它在分类性能上产生了更好的结果，生成了有意义的患者聚类，并在患者相似性搜索和预测中提供了一致的结果。
英文摘要：
In the rapidly evolving healthcare industry, platforms now have access to not only traditional medical records, but also diverse data sets encompassing various patient interactions, such as those from healthcare web portals. To address this rich diversity of data, we introduce WellFactor: a method that derives patient profiles by integrating information from these sources. Central to our approach is the utilization of constrained low-rank approximation. WellFactor is optimized to handle the sparsity that is often inherent in healthcare data. Moreover, by incorporating task-specific label information, our method refines the embedding results, offering a more informed perspective on patients. One important feature of WellFactor is its ability to compute embeddings for new, previously unobserved patient data instantaneously, eliminating the need to revisit the entire data set or recomputing the embedding. Comprehensive evaluations on real-world healthcare data demonstrate WellFactor's effectiveness. It produces better results compared to other existing methods in classification performance, yields meaningful clustering of patients, and delivers consistent results in patient similarity searches and predictions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
