@inproceedings{ceea50ee17bb488a8ec7c367534eb520,
title = "Predicting flare probability in rheumatoid arthritis using machine learning methods",
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%.",
keywords = "Electronic medical record, Flare probability, Predictive modeling, Rheumatoid arthritis",
author = "Asmir Voden{\v c}arevic and {VAn Der Goes}, {Marlies C.} and Medina, {O'Jay A.G.} and {De Groot}, {Mark C.H.} and Saskia Haitjema and {Van Solinge}, {Wouter W.} and Hoefer, {Imo E.} and Peelen, {Linda M.} and {Van Laar}, {Jacob M.} and Marcus Zimmermann-Rittereiser and Hamans, {Bob C.} and Welsing, {Paco M.J.}",
note = "Funding Information: This project was made possible by the Applied Data Analytics in Medicine (ADAM) programme of the University Medical Center Utrecht, Utrecht, the Netherlands. The authors would like to specifically acknowledge ir. Hyleco H. Nauta and Harry Pijl, MBA for their organizational support. Additionally, the authors would like to acknowledge Arjan Westrik from Accenture as well as Heike Bollmann and Bas Idzenga from Siemens Healthineers for their overall support to the ADAM-RA Project. We are grateful to rheumatologists of the UMC Utrecht for their valuable input regarding clinical definitions and suggestions for implementation during the project. Moreover, we thank the pharmacy of the UMC Utrecht for their valuable insights in the process of medication handling. Publisher Copyright: Copyright {\textcopyright} 2018 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.; 7th International Conference on Data Science, Technology and Applications, DATA 2018 ; Conference date: 26-07-2018 Through 28-07-2018",
year = "2018",
doi = "10.5220/0006930501870192",
language = "English",
series = "DATA 2018 - Proceedings of the 7th International Conference on Data Science, Technology and Applications",
publisher = "SciTePress",
pages = "187--192",
editor = "Jorge Bernardino and Christoph Quix",
booktitle = "Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,",
}