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Machine Learning to Predict Anti–Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers

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Machine Learning to Predict Anti–Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers

Arthritis & Rheumatology

Fecha de publicación: 24 July 2019

DOI: https://doi.org/10.1002/art.41056

Autores: Yuanfang Guan, Hongjiu Zhang, Daniel Quang, Ziyan Wang, Stephen C. J. Parker, Dimitrios A. Pappas, Joel M. Kremer, Fan Zhu

Background: Accurate prediction of treatment responses in rheumatoid arthritis (RA) patients can provide valuable information on effective drug selection. Anti–tumor necrosis factor (anti‐TNF) drugs are an important second‐line treatment after methotrexate, the classic first‐line treatment for RA. However, patient heterogeneity hinders identification of predictive biomarkers and accurate modeling of anti‐TNF drug responses. This study was undertaken to investigate the usefulness of machine learning to assist in developing predictive models for treatment response.

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