Using Machine Learning to Improve the Prediction of Functional Outcome in Ischemic Stroke Patients.

Miguel Monteiro,Ana Catarina Fonseca,Ana Teresa Freitas,Teresa Pinho E Melo,Alexandre P Francisco,Jose M Ferro,Arlindo L Oliveira

IEEE/ACM Transactions on Computational Biology and Bioinformatics（2018）

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摘要

Ischemic stroke is a leading cause of disability and death worldwide among adults. The individual prognosis after stroke is extremely dependent on treatment decisions physicians take during the acute phase. In the last five years, several scores such as the ASTRAL, DRAGON, and THRIVE have been proposed as tools to help physicians predict the patient functional outcome after a stroke. These scores ...

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关键词

Hospitals,Machine learning,Data models,Partitioning algorithms,Prognostics and health management,Learning systems,Predictive methods,Stroke (medical condition)

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