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Using natural language processing to identify symptoms in systemic mastocytosis.

Fagen Xie1, Kevin Y Tse2, Chantal C Avila1

  • 1Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States.

JAMIA Open
|November 14, 2025
PubMed
Summary

A new natural language processing (NLP) algorithm can identify rare systemic mastocytosis (SM) symptoms from clinical notes, aiding earlier diagnosis. This tool accurately extracts patient symptoms from electronic health records (EHRs), improving rare disease recognition.

Keywords:
clinical text miningelectronic health recordsnatural language processingrare diseasesymptom extractionsystemic mastocytosis

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Rare Diseases

Background:

  • Systemic mastocytosis (SM) is a rare, multisystem disease with varied symptoms, often leading to delayed diagnosis.
  • Unstructured clinical notes in electronic health records (EHRs) contain valuable symptom data but lack scalable extraction methods.
  • Earlier recognition of SM is hindered by challenges in systematically documenting and analyzing patient symptoms.

Purpose of the Study:

  • To develop and validate a natural language processing (NLP) algorithm for identifying 23 potential SM-related symptoms.
  • To assess the algorithm's effectiveness in extracting symptoms from unstructured EHR data for SM and comparison groups.
  • To evaluate the potential of NLP in improving the early detection of rare diseases like SM.

Main Methods:

  • A retrospective study utilized EHR data from Kaiser Permanente Southern California (2008-2023).
  • A rule-based NLP algorithm was developed using annotated training data and validated on double-annotated notes.
  • The algorithm was applied to a large cohort of electronic health records (EHRs) from SM, chronic spontaneous urticaria (CSU), and control patients.

Main Results:

  • The NLP algorithm demonstrated high precision and recall (over 90%) for most SM-related symptoms.
  • Lower precision was noted for "epigastric or abdominal bloating" and "swelling" due to ambiguous references.
  • Symptom documentation varied across groups, with SM patients showing more gastrointestinal and systemic symptoms, and CSU patients more cutaneous symptoms.

Conclusions:

  • Rule-based NLP is feasible for identifying SM symptoms from unstructured EHR narratives with strong performance.
  • The algorithm successfully extracted meaningful patterns in symptom documentation, supporting its utility for rare disease research.
  • NLP applications can enhance early recognition of rare diseases and inform data-driven healthcare strategies.