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Sebetralstat for on-demand treatment of hereditary angioedema: A pooled analysis of placebo-controlled clinical trials.

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Angioedema due to acquired C1 inhibitor deficiency: patient experience, conceptual disease model, and assessment of patient-reported outcome measures.

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Classification of angioedema types using decision tree modeling.

Felix Aulenbacher1,2, Annika Gutsche1,2, Henriette Farkas3

  • 1Angioedema Center of Reference and Excellence (ACARE), Institute of Allergology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.

Frontiers in Immunology
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

A machine learning model accurately identifies angioedema types, achieving 94% accuracy for hereditary angioedema (HAE). This AI tool aids in diagnosing the specific cause of swelling, improving patient care.

Keywords:
Random Forestangioedemabradykinin-mediated angioedemaclinical decision supportdiagnostic classificationhereditary angioedemamachine learningmast cell-mediated angioedema

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Area of Science:

  • Immunology
  • Computational Biology
  • Medical Diagnostics

Background:

  • Angioedema (AE) presents with localized swelling, but diverse causes, prognoses, and treatments complicate diagnosis.
  • Accurate identification of specific AE types is crucial for effective management.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for improving the diagnostic accuracy of various angioedema types.
  • To create an AI-driven tool to assist clinicians in differentiating between six types of AE.

Main Methods:

  • A Random Forest (RF) ML model was developed using clinical data from 342 patients across 12 European AE centers.
  • The model was trained on established AE characteristics, validated, and optimized through literature review and expert collaboration.

Main Results:

  • The optimized RF model achieved up to 94% true positive rate for hereditary angioedema (HAE) due to C1 inhibitor deficiency (C1INH).
  • Overall accuracy across six AE types was 89.2%, with a Kappa value of 81.8%, demonstrating high agreement with expert diagnoses.

Conclusions:

  • This study presents the first ML algorithm designed for pre-assessment to aid in angioedema diagnosis.
  • The developed ML model shows significant potential in accurately identifying AE types, supporting clinical decision-making.