Using natural language processing to identify symptoms in systemic mastocytosis.

ABSTRACT:

Background

Systemic mastocytosis (SM) is a rare disorder with heterogeneous, multisystem symptoms that often lead to diagnostic delays. Real-world symptom documentation in unstructured clinical notes may hold untapped potential for earlier recognition, but scalable extraction methods are lacking.

Objective

To develop and validate a natural language processing (NLP) algorithm to identify 23 potentially SM-related symptoms from unstructured electronic health record (EHR) documentation and assess its application across SM and comparison groups.

Methods

We conducted a retrospective study using EHR data from Kaiser Permanente Southern California (2008-2023). SM patients (n = 135) were matched 1:2:3 to chronic spontaneous urticaria (CSU) patients (n = 270) and non-SM/non-CS

SUBMITTER: Xie F 

PROVIDER: S-EPMC12605745 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature