A machine-learning model for reducing misdiagnosis in heparin-induced thrombocytopenia: A prospective, multicenter, observational study

BACKGROUND: Diagnosing heparin-induced thrombocytopenia (HIT) at the bedside remains challenging, exposing a significant number of patients at risk of delayed diagnosis or overtreatment. We hypothesized that machine-learning algorithms could be utilized to develop a more accurate and user-friendly d...

Descripción completa

Detalles Bibliográficos
Autores principales: Nilius, Henning, Cuker, Adam, Haug, Sigve, Nakas, Christos, Studt, Jan-Dirk, Tsakiris, Dimitrios A., Greinacher, Andreas, Mendez, Adriana, Schmidt, Adrian, Wuillemin, Walter A., Gerber, Bernhard, Kremer Hovinga, Johanna A., Vishnu, Prakash, Graf, Lukas, Kashev, Alexander, Sznitman, Raphael, Bakchoul, Tamam, Nagler, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9706528/
https://www.ncbi.nlm.nih.gov/pubmed/36457646
http://dx.doi.org/10.1016/j.eclinm.2022.101745

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9706528/
https://www.ncbi.nlm.nih.gov/pubmed/36457646
http://dx.doi.org/10.1016/j.eclinm.2022.101745

Ejemplares similares