Language-model-based patient embedding using electronic health records facilitates phenotyping, disease forecasting, and progression analysis.

ABSTRACT: Current studies regarding the secondary use of electronic health records (EHR) predominantly rely on domain expertise and existing medical knowledge. Though significant efforts have been devoted to investigating the application of machine learning algorithms in the EHR, efficient and powerful representation of patients is needed to unleash the potential of discovering new medical patterns underlying the EHR. Here, we present an unsupervised method for embedding high-dimensional EHR data at the patient level, aimed at characterizing patient heterogeneity in complex diseases and identifying new disease patterns associated with clinical outcome disparities. Inspired by the architecture of modern language models-specifically transformers with attention mechanisms, we use patient diagnosis and

SUBMITTER: Xian S 

PROVIDER: S-EPMC11469380 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature