Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study.

Journal: The Journal of dermatological treatment

PMID: 40107277

Abstract

BACKGROUND: Identifying the risk of psoriasis relapse after discontinuing biologics can help optimize treatment strategies, potentially reducing relapse rates and alleviating the burden of disease management.

Authors

Shan Huang

Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, China.
Yanping Bai

Department of Dermatology, China-Japan Friendship Hospital, National Center for Integrative Medicine, Beijing, 100029, People's Republic of China.
Ruozhou Qi

Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Hongda Yu

Department of Dermatology, China-Japan Friendship Hospital, National Center for Integrative Chinese and Western Medicine, Beijing, China.
Xingwu Duan

Yunnan Key Laboratory of Soil Erosion Prevention and Green Development, Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China.

Keywords

Adult Biological Products Cohort Studies Female Humans Machine Learning Male Middle Aged Nomograms Precision Medicine Psoriasis Recurrence Remission Induction Risk Assessment ROC Curve Severity of Illness Index Withholding Treatment

External Resources

View on PubMed Access via DOI PubMed (40107277)

Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study.

Abstract

Authors

Keywords

External Resources

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