Authors:
Asmir Vodenčarević
1
;
Marlies C. van der Goes
2
;
O’Jay A. G. Medina
3
;
Mark C. H. de Groot
4
;
Saskia Haitjema
4
;
Wouter W. van Solinge
4
;
Imo E. Hoefer
4
;
Linda M. Peelen
5
;
Jacob M. van Laar
2
;
Marcus Zimmermann-Rittereiser
1
;
Bob C. Hamans
6
and
Paco M. J. Welsing
2
Affiliations:
1
Digital Services, Siemens Healthcare GmbH, Erlangen and Germany
;
2
Department of Rheumatology & Clinical Immunology, UMC Utrecht, Utrecht University, Utrecht and The Netherlands
;
3
Department of Information Technology, UMC Utrecht, Utrecht University, Utrecht and The Netherlands
;
4
Department of Clinical Chemistry and Hematology, UMC Utrecht, Utrecht University, Utrecht and The Netherlands
;
5
Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht and The Netherlands
;
6
Enterprise Services & Solutions, Siemens Healthcare Nederland B.V., The Hague and The Netherlands
Keyword(s):
Predictive Modeling, Flare Probability, Rheumatoid Arthritis, Electronic Medical Record.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Knowledge-Based Systems
;
Predictive Modeling
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
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 proba
bility 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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