Machine learning to predict venous thrombosis in acutely ill medical patients

Machine learning to predict venous thrombosis in acutely ill medical patients

Abstract 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...

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Bibliographic Details
Main Authors: Tarek Nafee, C. Michael Gibson, Ryan Travis, Megan K. Yee, Mathieu Kerneis, Gerald Chi, Fahad AlKhalfan, Adrian F. Hernandez, Russell D. Hull, Ander T. Cohen, Robert A. Harrington, Samuel Z. Goldhaber
Format: Article
Language:English
Published: Wiley 2020-02-01
Series:Research and Practice in Thrombosis and Haemostasis
Subjects:
acute medically ill
machine learning
personalized medicine
super learner
venous thromboembolism
Online Access:https://doi.org/10.1002/rth2.12292

Internet

https://doi.org/10.1002/rth2.12292

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