Prediction of Adverse Events in Stable Non-Variceal Gastrointestinal Bleeding Using Machine Learning

Clinical risk-scoring systems are important for identifying patients with upper gastrointestinal bleeding (UGIB) who are at a high risk of hemodynamic instability. We developed an algorithm that predicts adverse events in patients with initially stable non-variceal UGIB using machine learning (ML)....

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Detalles Bibliográficos
Autores principales: Seo, Dong-Woo, Yi, Hahn, Park, Beomhee, Kim, Youn-Jung, Jung, Dae Ho, Woo, Ilsang, Sohn, Chang Hwan, Ko, Byuk Sung, Kim, Namkug, Kim, Won Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7464777/
https://www.ncbi.nlm.nih.gov/pubmed/32796647
http://dx.doi.org/10.3390/jcm9082603

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7464777/
https://www.ncbi.nlm.nih.gov/pubmed/32796647
http://dx.doi.org/10.3390/jcm9082603

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