Identification of clinical phenotypes and disease trajectories in SLE using AI through a natural language processing framework

Electronic health records (EHRs) contain a wealth of unstructured patient data that can be leveraged using artificial intelligence (AI). This study aimed to develop a natural language processing (NLP) pipeline to identify clinical phenotypes and disease trajectories in patients with systemic lupus erythematosus (SLE) from EHRs.

EHR data from SLE patients were included. A standardized stepwise framework combining AI and human intelligence (HI) was designed. Ontology-based definitions were developed for clinical domains, flares and disease complexity phenotypes (low, medium, high) at the first contact, and corresponding data were extracted using an NLP-based pipeline.