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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Dades bibliogràfiques
Publicat a:Res Pract Thromb Haemost
Autors principals: 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.
Format: Artigo
Idioma:Inglês
Publicat: John Wiley and Sons Inc. 2020
Matèries:
Original Articles: Thrombosis
Accés en línia:https://ncbi.nlm.nih.gov/pmc/articles/PMC7040551/
https://ncbi.nlm.nih.gov/pubmed/32110753
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/rth2.12292
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