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

Hurault, Guillem; Domínguez‐Hüttinger, Elisa; Langan, Sinéad M.; Williams, Hywel C.; Tanaka, Reiko J.

Authors

Guillem Hurault

Elisa Domínguez‐Hüttinger

Sinéad M. Langan

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HYWEL WILLIAMS hywel.williams@nottingham.ac.uk
Professor of Dermato-Epidemiology

Reiko J. Tanaka



Abstract

Background

Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalised 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.

Objective

We aimed to develop a proof‐of‐principle mechanistic machine learning model that predicts the patient‐specific evolution of AD severity scores on a daily basis.

Methods

We designed a probabilistic predictive model and trained it using Bayesian inference with the longitudinal data from two published clinical studies. The data consisted of daily recordings of AD severity scores and treatments used by 59 and 334 AD children over 6 months and 16 weeks, respectively. Validation of the predictive model was conducted in a forward‐chaining setting.

Results

Our model was able to predict future severity scores at the individual level and improved chance‐level forecast by 60%. Heterogeneous patterns in severity trajectories were captured with patient‐specific parameters such as the short‐term persistence of AD severity and responsiveness to topical steroids, calcineurin inhibitors and step‐up treatment.

Conclusions

Our proof of principle model successfully predicted the daily evolution of AD severity scores at an individual level, and could inform the design of personalised treatment strategies that can be tested in future studies. Our model‐based approach can be applied to other diseases such as asthma with apparent unpredictability and large variation in symptoms and treatment responses

Citation

Hurault, G., Domínguez‐Hüttinger, E., Langan, S. M., Williams, H. C., & Tanaka, R. J. (2020). Personalised prediction of daily eczema severity scores using a mechanistic machine learning model. Clinical and Experimental Allergy, 50(11), 1258-1266. https://doi.org/10.1111/cea.13717

Journal Article Type Article
Acceptance Date Jul 6, 2020
Online Publication Date Sep 9, 2020
Publication Date Nov 1, 2020
Deposit Date Aug 18, 2020
Publicly Available Date Sep 9, 2020
Journal Clinical & Experimental Allergy
Print ISSN 0954-7894
Electronic ISSN 1365-2222
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 50
Issue 11
Pages 1258-1266
DOI https://doi.org/10.1111/cea.13717
Keywords Immunology; Immunology and Allergy
Public URL https://nottingham-repository.worktribe.com/output/4841981
Publisher URL https://onlinelibrary.wiley.com/doi/full/10.1111/cea.13717

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