Harmonizing Multi-Institutional Clinical Documentation Using Natural Language Processing in Neurofibromatosis Type 1

Machine learning (ML) and natural language processing (NLP) approaches are increasingly used to support nuanced phenotyping, surveillance, and trial readiness using electronic health records (EHRs) in neurologic disease. However, inconsistent clinical documentation limits data harmonization and model performance, particularly in complex heterogeneous disorders such as neurofibromatosis type 1 (NF1). The primary research question was whether physician-authored EHR documentation of NF1-related features demonstrates systematic lexical variation that may impede computational phenotyping. The primary objective was to characterize lexical variation and documentation completeness for core NF1 features, while a secondary objective aimed to develop a standardized, data-informed clinical lexicon aligned with contemporary clinical practice and terminology standards.