New risk model is able to identify patients with a low risk of progression in systemic sclerosis

OBJECTIVES: To develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity. METHODS: A machine learning approach was used to develop a model that can identify patients without disease progression. SSc patients included in the...

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Detalles Bibliográficos
Autores principales: van Leeuwen, Nina Marijn, Maurits, Marc, Liem, Sophie, Ciaffi, Jacopo, Ajmone Marsan, Nina, Ninaber, Maarten, Allaart, Cornelia, Gillet van Dongen, Henrike, Goekoop, Robbert, Huizinga, Tom, Knevel, Rachel, De Vries-Bouwstra, Jeska
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Systemic Sclerosis
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8169494/
https://www.ncbi.nlm.nih.gov/pubmed/34059523
http://dx.doi.org/10.1136/rmdopen-2020-001524

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8169494/
https://www.ncbi.nlm.nih.gov/pubmed/34059523
http://dx.doi.org/10.1136/rmdopen-2020-001524

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