Predicting Flare Probability in Rheumatoid Arthritis using Machine Learning Methods

Asmir Vodenčarević, Marlies C. van der Goes, O’Jay A. G. Medina, Mark C. H. de Groot, Saskia Haitjema, Wouter W. van Solinge, Imo E. Hoefer, Linda M. Peelen, Jacob M. van Laar, Marcus Zimmermann-Rittereiser, Bob C. Hamans, Paco M. J. Welsing

Abstract

Rheumatoid Arthritis (RA) is a chronic inflammatory disease that mostly affects joints. It requires life-long treatment aiming at suppression of disease activity. RA is characterized by periods of low or even absent activity of the disease (“remission”) alternated with exacerbations of the disease (“flares”) leading to pain, functional limitations and decreased quality of life. Flares and periods of high disease activity can lead to joint damage and permanent disability. Over the last decades treatment of RA patients has improved, especially with the new “biological” drugs. This expensive medication also carries a risk of serious adverse events such as severe infections. Therefore patients and physicians often wish to taper the dose or even stop the drug, once stable remission is reached. Unfortunately, drug tapering is associated with the increased risk of flares. In this paper we applied machine learning methods on the Utrecht Patient Oriented Database (UPOD) to predict flare probability within a time horizon of three months. Providing information about flare probability for different dose reduction scenarios would enable clinicians to perform informed tapering which may prevent flares, reduce adverse events and save drug costs. Our best models can predict flares with AUC values of about 80%.

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Paper Citation


in Harvard Style

Goes M., Medina O., Groot M., Haitjema S., Solinge W., Hoefer I., Peelen L., Laar J., Zimmermann-Rittereiser M., Hamans B. and Welsing P. (2018). Predicting Flare Probability in Rheumatoid Arthritis using Machine Learning Methods.In Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-318-6, pages 187-192. DOI: 10.5220/0006930501870192


in Bibtex Style

@conference{data18,
author={Marlies C. van der Goes and O’Jay A. G. Medina and Mark C. H. de Groot and Saskia Haitjema and Wouter W. van Solinge and Imo E. Hoefer and Linda M. Peelen and Jacob M. van Laar and Marcus Zimmermann-Rittereiser and Bob C. Hamans and Paco M. J. Welsing},
title={Predicting Flare Probability in Rheumatoid Arthritis using Machine Learning Methods},
booktitle={Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2018},
pages={187-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006930501870192},
isbn={978-989-758-318-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Predicting Flare Probability in Rheumatoid Arthritis using Machine Learning Methods
SN - 978-989-758-318-6
AU - Goes M.
AU - Medina O.
AU - Groot M.
AU - Haitjema S.
AU - Solinge W.
AU - Hoefer I.
AU - Peelen L.
AU - Laar J.
AU - Zimmermann-Rittereiser M.
AU - Hamans B.
AU - Welsing P.
PY - 2018
SP - 187
EP - 192
DO - 10.5220/0006930501870192