A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients

A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients

Background: Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories. Digital twin models, which forecast entire trajectories of patient health data, have potential applications in clinical trials and patient...

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Bibliographic Details
Main Authors: Angier Allen, Anna Siefkas, Emily Pellegrini, Hoyt Burdick, Gina Barnes, Jacob Calvert, Qingqing Mao, Ritankar Das
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Applied Sciences
Subjects:
digital twins
variational autoencoder
machine learning
algorithm
stroke
disease forecasting
Online Access:https://www.mdpi.com/2076-3417/11/12/5576

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https://www.mdpi.com/2076-3417/11/12/5576

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