Machine learning in action: stroke diagnosis and outcome prediction
The application of machine learning has rapidly evolved in medicine over the past decade.
In stroke, commercially available machine learning algorithms have already been …
In stroke, commercially available machine learning algorithms have already been …
A systematic review of machine learning models for predicting outcomes of stroke with structured data
Background and purpose Machine learning (ML) has attracted much attention with the hope
that it could make use of large, routinely collected datasets and deliver accurate …
that it could make use of large, routinely collected datasets and deliver accurate …
Machine learning–based model for prediction of outcomes in acute stroke
Background and Purpose—The prediction of long-term outcomes in ischemic stroke patients
may be useful in treatment decisions. Machine learning techniques are being increasingly …
may be useful in treatment decisions. Machine learning techniques are being increasingly …
Multimodal predictive modeling of endovascular treatment outcome for acute ischemic stroke using machine-learning
G Brugnara, U Neuberger, MA Mahmutoglu, M Foltyn… - Stroke, 2020 - Am Heart Assoc
Background and Purpose: This study assessed the predictive performance and relative
importance of clinical, multimodal imaging, and angiographic characteristics for predicting …
importance of clinical, multimodal imaging, and angiographic characteristics for predicting …
[HTML][HTML] Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury
Objective We aimed to explore the added value of common machine learning (ML)
algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study …
algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study …
Artificial intelligence applications in stroke
K Mouridsen, P Thurner, G Zaharchuk - Stroke, 2020 - Am Heart Assoc
Management of stroke highly depends on informa-tion from imaging studies. Noncontrast
computed tomography (CT) and magnetic resonance imaging (MRI) can both be used to …
computed tomography (CT) and magnetic resonance imaging (MRI) can both be used to …
Predicting clinical outcomes of large vessel occlusion before mechanical thrombectomy using machine learning
Background and Purpose—The clinical course of acute ischemic stroke with large vessel
occlusion (LVO) is a multifactorial process with various prognostic factors. We aimed to …
occlusion (LVO) is a multifactorial process with various prognostic factors. We aimed to …
Artificial intelligence for decision support in acute stroke—current roles and potential
The identification and treatment of patients with stroke is becoming increasingly complex as
more treatment options become available and new relationships between disease features …
more treatment options become available and new relationships between disease features …
Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke
Abstract Treatment selection is becoming increasingly more important in acute ischemic
stroke patient care. Clinical variables and radiological image biomarkers (old age, pre …
stroke patient care. Clinical variables and radiological image biomarkers (old age, pre …
Functional outcome prediction in ischemic stroke: a comparison of machine learning algorithms and regression models
Background and Purpose: Stroke-related functional risk scores are used to predict patients'
functional outcomes following a stroke event. We evaluate the predictive accuracy of …
functional outcomes following a stroke event. We evaluate the predictive accuracy of …