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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Xehetasun bibliografikoak
Egile Nagusiak: Angier Allen, Anna Siefkas, Emily Pellegrini, Hoyt Burdick, Gina Barnes, Jacob Calvert, Qingqing Mao, Ritankar Das
Formatua: Artikulua
Hizkuntza:English
Argitaratua: MDPI AG 2021-06-01
Saila:Applied Sciences
Gaiak:
digital twins
variational autoencoder
machine learning
algorithm
stroke
disease forecasting
Sarrera elektronikoa:https://www.mdpi.com/2076-3417/11/12/5576

Internet

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

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