Personalized prediction of daily eczema severity scores using a mechanistic machine learning model.

Journal: Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology

PMID: 32750186

Abstract

BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalized treatment strategies for AD is challenging, given the apparent unpredictability and large variation in AD symptoms and treatment responses within and across individuals. Better prediction of AD severity over time for individual patients could help to select optimum timing and type of treatment for improving disease control.

Authors

Guillem Hurault

Department of Bioengineering, Imperial College London, London, UK.
Elisa Domínguez-Hüttinger

Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, México, México.
Sinéad M Langan

Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK.
Hywel C Williams

Population and Lifespan Health, University of Nottingham, Nottingham, UK.
Reiko J Tanaka

Department of Bioengineering, Imperial College London, London, United Kingdom.

Keywords

Bayes Theorem Dermatitis, Atopic Diagnosis, Computer-Assisted Humans Machine Learning Predictive Value of Tests Probability Proof of Concept Study Reproducibility of Results Severity of Illness Index Time Factors Treatment Outcome

External Resources

View on PubMed Access via DOI PubMed (32750186)

Personalized prediction of daily eczema severity scores using a mechanistic machine learning model.

Abstract

Authors

Keywords

External Resources

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