Unsupervised identification of asthma symptom subtypes supports treatable traits approach.

Journal: Allergology international : official journal of the Japanese Society of Allergology
Published Date: Jul 28, 2025

Abstract

BACKGROUND: Heterogeneity of asthma requires a personalized therapeutic approach. However, objective measurements, such as spirometry and fraction of exhaled nitric oxide (FeNO) for implementing treatable traits approach, are limited in low- and middle-income countries and non-specialist settings. To implement precision medicine even with minimal resources, we developed an algorithm using unsupervised machine learning techniques that estimates key treatable traits (airflow limitation, type 2 [T2] inflammation, and frequent exacerbations) based on an asthma patient-reported outcome (PRO).

Authors

  • Kazuki Hamada
    Department of Systems Bioinformatics, Graduate School of Medicine, Yamaguchi University, Yamaguchi, Japan; Department of Respiratory Medicine and Infectious Disease, Graduate School of Medicine, Yamaguchi University, Yamaguchi, Japan.
  • Takeshi Abe
    AI Systems Medicine Research and Training Center (AISMEC), Yamaguchi University Graduate School of Medicine, and Yamaguchi University Hospital.
  • Keiji Oishi
    Department of Respiratory Medicine and Infectious Disease, Graduate School of Medicine, Yamaguchi University, Yamaguchi, Japan.
  • Yoriyuki Murata
    Department of Respiratory Medicine and Infectious Disease, Graduate School of Medicine, Yamaguchi University, Yamaguchi, Japan.
  • Tsunahiko Hirano
    Department of Respiratory Medicine and Infectious Disease, Graduate School of Medicine, Yamaguchi University, Yamaguchi, Japan.
  • Takahide Hayano
    Department of Systems Bioinformatics, Yamaguchi University Graduate School of Medicine.
  • Masahiko Nakatsui
    AI Systems Medicine Research and Training Center, Graduate School of Medicine and University Hospital, Yamaguchi University, Yamaguchi, Japan; The Division of Systems Medicine and Informatics, Research Institute for Cell Design Medical Science, Yamaguchi University, Yamaguchi, Japan.
  • Yoshiyuki Asai
    Department of Systems Bioinformatics, Yamaguchi University Graduate School of Medicine.
  • Kazuto Matsunaga
    Department of Respiratory Medicine and Infectious Disease, Graduate School of Medicine, Yamaguchi University, Japan.

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