Machine Learning Identifies an Association Between Pre-existing Radiographic Damage and Long-term Clinical Outcomes with Secukinumab Therapy in Patients with Psoriatic Arthritis
Mease P, Van Der Heijde D, Kirkham B, Schett G, Orbai AM, Ritchlin C, Merola J, Pricop L, Zhu X, James D, Ligozio G (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: WILEY
City/Town: HOBOKEN
Conference Proceedings Title: ARTHRITIS & RHEUMATOLOGY
Authors with CRIS profile
Involved external institutions
Novartis AG
Switzerland (CH)
Harvard University
United States (USA) (US)
Leiden University
Netherlands (NL)
University of Rochester (UR)
United States (USA) (US)
Guy's and St Thomas' (NHS Foundation Trust)
United Kingdom (GB)
Johns Hopkins University (JHU)
United States (USA) (US)
Switzerland (CH)
Harvard University
United States (USA) (US)
Leiden University
Netherlands (NL)
University of Rochester (UR)
United States (USA) (US)
Guy's and St Thomas' (NHS Foundation Trust)
United Kingdom (GB)
Johns Hopkins University (JHU)
United States (USA) (US)
How to cite
APA:
Mease, P., Van Der Heijde, D., Kirkham, B., Schett, G., Orbai, A.-M., Ritchlin, C.,... Ligozio, G. (2020). Machine Learning Identifies an Association Between Pre-existing Radiographic Damage and Long-term Clinical Outcomes with Secukinumab Therapy in Patients with Psoriatic Arthritis. In ARTHRITIS & RHEUMATOLOGY. HOBOKEN: WILEY.
MLA:
Mease, Philip, et al. "Machine Learning Identifies an Association Between Pre-existing Radiographic Damage and Long-term Clinical Outcomes with Secukinumab Therapy in Patients with Psoriatic Arthritis." Proceedings of the ARTHRITIS & RHEUMATOLOGY HOBOKEN: WILEY, 2020.
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