ArticleLife (Basel, Switzerland)2022
Using Machine Learning to Detect Theranostic Biomarkers Predicting Respiratory Treatment Response.
Article in Life (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 6 citations in OpenAlex.
- Innovative Applications and Challenges of Artificial Intelligence in the Whole-Course Management of Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2026Review
- An Automated Method of Causal Inference of the Underlying Cause of Death of Citizens.Life (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundTheranostic approaches-the use of diagnostics for developing targeted therapies-are gaining popularity in the field of precision medicine. They are predominately used in cancer research, whereas there is little evidence of their use in respiratory medicine. This study aims to detect theranostic biomarkers associated with respiratory-treatment responses. This will advance theory and practice on the use of biomarkers in the diagnosis of respiratory diseases and contribute to developing targeted treatments.
methodsWe performed a cross-sectional analysis on a sample of 13,102 adults from the UK household longitudinal study 'Understanding Society'. We used recursive feature selection to identify 16 biomarkers associated with respiratory treatment responses. We then implemented several machine learning algorithms using the identified biomarkers as well as age, sex, body mass index, and lung function to predict treatment response.
resultsOur analysis shows that subjects with increased levels of alkaline phosphatase, glycated haemoglobin, high-density lipoprotein cholesterol, c-reactive protein, triglycerides, hemoglobin, and Clauss fibrinogen are more likely to receive respiratory treatments, adjusting for age, sex, body mass index, and lung function.
conclusionsThese findings offer a valuable blueprint on why and how the use of biomarkers as diagnostic tools can prove beneficial in guiding treatment management in respiratory diseases.
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