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Asthma is a chronic respiratory ailment that requires careful management due to its varying symptoms and influencing factors. It is characterized by airway inflammation, bronchial hyperresponsiveness, and reversible airflow obstruction, leading to symptoms like wheezing, shortness of breath, chest tightness, and coughing. The symptom frequency and intensity may vary considerably over time. It is also linked to immune system responses to allergens and irritants, highlighting the complex...
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Asthma is a chronic pulmonary condition involving inflammation of the airways, hyper-reactivity, and reversible obstruction of the airways. This condition can significantly impact a person's quality of life, making breathing difficult and leading to distressing symptoms.
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Inhaled Medications01:23

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Inhaled medications are crucial for managing chronic obstructive pulmonary disease (COPD) and asthma. They are essential for effective treatment and control, ensuring optimal respiratory health and well-being. Inhaled medication delivers drugs directly to the lungs, providing a rapid onset of action and reducing systemic side effects compared to oral or injectable medications. Three primary types of inhalation devices are used to administer these medications: nebulizers, metered-dose inhalers...
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The nursing management of asthma is a comprehensive approach that relies heavily on the expertise and dedication of healthcare professionals. It involves thorough assessment, accurate diagnosis, strategic planning, effective implementation, and diligent evaluation. By meticulously following this step-by-step process, healthcare professionals play a crucial role in providing the best possible care and treatment for patients with asthma, enhancing their overall health and well-being.
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Asthma is a chronic respiratory condition for which new therapeutic avenues, including anti-inflammatory drugs like mast cell stabilizers and anti-IgE treatments, continue to be developed.
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A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection.

Flory L Nkoy1, Bryan L Stone1, Yue Zhang2,3

  • 1Department of Pediatrics, University of Utah, Salt Lake City, UT, United States.

JMIR Medical Informatics
|April 17, 2024
PubMed
Summary
This summary is machine-generated.

Choosing the right inhaled corticosteroid (ICS) is crucial for asthma control. This study proposes a machine learning model using electronic health record data to predict patient response to specific ICS medications, moving towards personalized asthma care.

Keywords:
ICSartificial intelligenceasthmacausal inferencecorticosteroidcorticosteroidscustomizeddecision supportdrugdrugsforecastforecastinginhaledinhaled corticosteroidinhalermachine learningmedicationmedication selectionmedicationspersonalizedpharmaceuticpharmaceuticalpharmaceuticalspharmaceuticspharmaciespharmacologypharmacotherapypharmacypulmonaryrespiratory

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Area of Science:

  • Pulmonology and Respiratory Medicine
  • Pharmacogenomics and Personalized Medicine
  • Health Informatics and Machine Learning

Background:

  • Inhaled corticosteroids (ICS) are standard for persistent asthma, but suboptimal control affects many patients.
  • Asthma heterogeneity and genetic variations lead to variable ICS response, yet ICS selection is often non-personalized.
  • Current ICS prescribing relies on trial-and-error, cost, or policy, not patient-specific factors, leading to treatment gaps.

Purpose of the Study:

  • To address the need for a decision support tool for selecting the most effective inhaled corticosteroid (ICS) for individual asthma patients.
  • To bridge the gap in predicting patient response to specific ICS medications.
  • To lay the groundwork for personalized asthma management by predicting ICS effectiveness.

Main Methods:

  • Development of a machine learning model utilizing electronic health record (EHR) data to predict individual patient ICS response.
  • Application of causal inference techniques to analyze patient characteristics and forecast ICS effectiveness over the next year.
  • Leveraging readily available EHR data as a cost-effective method to profile patients, mirroring genotypic or phenotypic information.

Main Results:

  • The study outlines a novel approach to predict patient-specific inhaled corticosteroid (ICS) response using machine learning and EHR data.
  • The proposed model aims to identify patterns within EHR data that correlate with patient endotypes or genotypes, influencing ICS efficacy.
  • This research establishes a roadmap for developing a predictive tool to guide ICS selection at the point of care.

Conclusions:

  • A significant gap exists in personalized inhaled corticosteroid (ICS) selection for asthma management.
  • Machine learning models analyzing EHR data offer a feasible strategy to predict individual ICS response and improve asthma control.
  • The ultimate goal is to transition from a one-size-fits-all approach to personalized asthma care, enhancing patient outcomes and optimizing healthcare resources.