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Childhood Asthma: Advances Using Machine Learning and Mechanistic Studies.
Sejal Saglani1, Adnan Custovic2
11 National Heart and Lung Institute and.
American Journal of Respiratory and Critical Care Medicine
|December 21, 2018
Summary
Childhood asthma is not a single disease but has multiple types. Understanding these differences and early life exposures like microbiome changes can lead to better prevention and treatments.
Area of Science:
- Immunology
- Pediatrics
- Data Science
Background:
- Childhood asthma is recognized as an aggregated diagnosis with diverse underlying pathophysiologies.
- Advances in understanding causal mechanisms present an opportunity to reduce asthma's burden.
Purpose of the Study:
- To leverage data-driven methodologies for discovering hidden structures in healthcare data to generate new hypotheses.
- To translate these findings into clinical practice by linking phenotypes to mechanisms and clinical presentations.
Main Methods:
- Utilizing data-driven methodologies and big healthcare data analysis.
- Conducting epidemiological, cohort, and mechanistic studies in humans and mouse models.
- Facilitating iterative dialogue between data scientists and medical professionals, epidemiologists, basic scientists, and geneticists.
Main Results:
- Evidence suggests environmental exposures like traditional farming may protect against asthma by modulating innate immunity.
- Impaired innate immunity appears to increase susceptibility to asthma.
- Early life microbiome modulation shows potential for conferring immune protection.
Conclusions:
- Recognizing asthma's heterogeneity is crucial for developing targeted prevention and treatment strategies.
- Mechanism-based, stratified approaches informed by big data and interdisciplinary collaboration are key to advancing asthma care.
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