4.7 Article

Random forest-based prediction of stroke outcome

Related references

Note: Only part of the references are listed.
Article Computer Science, Interdisciplinary Applications

Evaluation of machine learning methods to stroke outcome prediction using a nationwide disease registry

Ching-Heng Lin et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2020)

Article Computer Science, Interdisciplinary Applications

A Random Forest classifier-based approach in the detection of abnormalities in the retina

Amrita Roy Chowdhury et al.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2019)

Article Computer Science, Interdisciplinary Applications

ECG-based pulse detection during cardiac arrest using random forest classifier

Andoni Elola et al.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2019)

Article Engineering, Biomedical

Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation

Torgyn Shaikhina et al.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2019)

Review Health Care Sciences & Services

A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models

Evangelia Christodoulou et al.

JOURNAL OF CLINICAL EPIDEMIOLOGY (2019)

Article Biochemical Research Methods

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

Miguel Monteiro et al.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2018)

Article Clinical Neurology

Recent Advances in the Acute Management of Intracerebral Hemorrhage

Joseph D. Burns et al.

NEUROSURGERY CLINICS OF NORTH AMERICA (2018)

Article Emergency Medicine

Effects of Telestroke on Thrombolysis Times and Outcomes: A Meta-analysis

Alireza Baratloo et al.

PREHOSPITAL EMERGENCY CARE (2018)

Article Clinical Neurology

Trends in stroke outcomes in the last ten years in a European tertiary hospital

Emilio Rodriguez-Castro et al.

BMC NEUROLOGY (2018)

Article Computer Science, Artificial Intelligence

Breast cancer diagnosis using GA feature selection and Rotation Forest

Emina Alickovic et al.

NEURAL COMPUTING & APPLICATIONS (2017)

Article Endocrinology & Metabolism

Fully automated stroke tissue estimation using random forest classifiers (FASTER)

Richard McKinley et al.

JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM (2017)

Article Computer Science, Artificial Intelligence

Experimental study and Random Forest prediction model of microbiome cell surface hydrophobicity

Yong Liu et al.

EXPERT SYSTEMS WITH APPLICATIONS (2017)

Review Neurosciences

Desmoteplase for Acute Ischemic Stroke: A Systematic Review and Metaanalysis of Randomized Controlled Trials

Ahmed Elmaraezy et al.

CNS & NEUROLOGICAL DISORDERS-DRUG TARGETS (2017)

Article Medicine, General & Internal

Epidemiology of stroke in Europe and trends for the 21st century

Yannick Bejot et al.

PRESSE MEDICALE (2016)

Article Biochemical Research Methods

Extra Tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences

Oskar Maier et al.

JOURNAL OF NEUROSCIENCE METHODS (2015)

Article Neuroimaging

Prediction of stroke thrombolysis outcome using CT brain machine learning

Paul Bentley et al.

NEUROIMAGE-CLINICAL (2014)

Review Rehabilitation

MULTIDISCIPLINARY CARE FOR STROKE PATIENTS LIVING IN THE COMMUNITY: A SYSTEMATIC REVIEW

Manon Fens et al.

JOURNAL OF REHABILITATION MEDICINE (2013)

Review Clinical Neurology

Prediction of recovery of motor function after stroke

Cathy Stinear

LANCET NEUROLOGY (2010)

Review Biochemical Research Methods

A review of feature selection techniques in bioinformatics

Yvan Saeys et al.

BIOINFORMATICS (2007)

Review Endocrinology & Metabolism

Pathophysiology of cerebral ischemia and brain trauma: Similarities and differences

HM Bramlett et al.

JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM (2004)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)