Machine learning to predict early TNF inhibitor users in patients with ankylosing spondylitis.

Scientific Reports
Seulkee LeeJaejoon Lee

Abstract

We aim to generate an artificial neural network (ANN) model to predict early TNF inhibitor users in patients with ankylosing spondylitis. The baseline demographic and laboratory data of patients who visited Samsung Medical Center rheumatology clinic from Dec. 2003 to Sep. 2018 were analyzed. Patients were divided into two groups: early-TNF and non-early-TNF users. Machine learning models were formulated to predict the early-TNF users using the baseline data. Feature importance analysis was performed to delineate significant baseline characteristics. The numbers of early-TNF and non-early-TNF users were 90 and 505, respectively. The performance of the ANN model, based on the area under curve (AUC) for a receiver operating characteristic curve (ROC) of 0.783, was superior to logistic regression, support vector machine, random forest, and XGBoost models (for an ROC curve of 0.719, 0.699, 0.761, and 0.713, respectively) in predicting early-TNF users. Feature importance analysis revealed CRP and ESR as the top significant baseline characteristics for predicting early-TNF users. Our model displayed superior performance in predicting early-TNF users compared with logistic regression and other machine learning models. Machine learning ...Continue Reading

References

May 3, 2002·The New England Journal of Medicine·Jennifer D GormanJohn C Davis
Feb 9, 2006·Annals of the Rheumatic Diseases·T PhamD van der Heijde
Dec 21, 2018·Journal of the American Heart Association·Ioannis A KakadiarisMorteza Naghavi

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Citations

Nov 4, 2021·Nature Reviews. Rheumatology·Kathryn M KingsmorePeter E Lipsky

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Methods Mentioned

BETA
ESR

Software Mentioned

Scikit
Python
matplotlib
XGBoost
learn
RF

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