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Poster De Conférence Année : 2024

To treat or not to treat: Predicting Asthma Patient' Responsiveness to Omalizumab Using Machine Learning

Résumé

Not being well controlled by therapy with inhaled corticosteroids and long-acting β2 agonist bronchodilators is a major concern for severe-asthma patients. The current treatment option for these patients is the use of biologicals such as anti-IgE treatment, omalizumab, as an add-on therapy. Despite the accepted use of omalizumab, patients do not always benefit from it. Therefore, there is a need to identify reliable biomarkers as predictors of omalizumab response.
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hal-04634934 , version 1 (04-07-2024)

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  • HAL Id : hal-04634934 , version 1

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Sarah Kidwai, Pietro Barbiero, Irma Meijerman, Alberto Tonda, Paula Perez-Pardo, et al.. To treat or not to treat: Predicting Asthma Patient' Responsiveness to Omalizumab Using Machine Learning. UIPS biennial symposium 2024, Jan 2024, Utrecht, Netherlands. ⟨hal-04634934⟩
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