Machine learning to predict venous thrombosis in acutely ill medical patients

BACKGROUND: The identification of acutely ill patients at high risk for venous thromboembolism (VTE) may be determined clinically or by use of integer‐based scoring systems. These scores demonstrated modest performance in external data sets. OBJECTIVES: To evaluate the performance of machine learnin...

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Detalles Bibliográficos
Autores principales: Nafee, Tarek, Gibson, C. Michael, Travis, Ryan, Yee, Megan K., Kerneis, Mathieu, Chi, Gerald, AlKhalfan, Fahad, Hernandez, Adrian F., Hull, Russell D., Cohen, Ander T., Harrington, Robert A., Goldhaber, Samuel Z.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Original Articles: Thrombosis
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7040551/
https://www.ncbi.nlm.nih.gov/pubmed/32110753
http://dx.doi.org/10.1002/rth2.12292

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7040551/
https://www.ncbi.nlm.nih.gov/pubmed/32110753
http://dx.doi.org/10.1002/rth2.12292

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