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Puslapis 1 nuo 503 rezultatus
A thrombolytic decision tree.
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We constructed a decision analysis model based on data in the medical literature to estimate the possible outcomes of thrombolytic therapy in patients 50 to 80 years old with possible myocardial infarction. We used the model to test the most likely effects of treatment (determined by averaging the
[Hyperdense carotid T in stroke: 'tree sign'].
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COVID-19-Related Stroke: Barking up the Wrong Tree?
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Predicting stroke inpatient rehabilitation outcome using a classification tree approach.
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A classification tree, a nonparametric statistical analysis, was used to develop decision rules to predict a favorable inpatient stroke rehabilitation outcome. Descriptive and functional status data collected on admission from 225 patients were the predictor variables. Favorable outcome was defined
Using T3, an improved decision tree classifier, for mining stroke-related medical data.
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OBJECTIVE
Medical data are a valuable resource from which novel and potentially useful knowledge can be discovered by using data mining. Data mining can assist and support medical decision making and enhance clinical management and investigative research. The objective of this work is to propose a
[Identification of subgroups with lower level of stroke knowledge using decision-tree analysis].
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OBJECTIVE
This study was performed to explore levels of stroke knowledge and identify subgroups with lower levels of stroke knowledge among adults in Korea.
METHODS
A cross-sectional survey was used and data were collected in 2012. A national sample of 990 Koreans aged 20 to 74 years participated in
Extra tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences.
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BACKGROUND
To analyse the relationship between structure and (dys-)function of the brain after stroke, accurate and repeatable segmentation of the lesion area in magnetic resonance (MR) images is required. Manual delineation, the current gold standard, is time consuming and suffers from high intra-
Boosted Tree Model Reforms Multimodal Magnetic Resonance Imaging Infarct Prediction in Acute Stroke.
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OBJECTIVE
Stroke imaging is pivotal for diagnosis and stratification of patients with acute ischemic stroke to treatment. The potential of combining multimodal information into reliable estimates of outcome learning calls for robust machine learning techniques with high flexibility and accuracy. We
Better Diffusion Segmentation in Acute Ischemic Stroke Through Automatic Tree Learning Anomaly Segmentation.
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Stroke is the second most common cause of death worldwide, responsible for 6.24 million deaths in 2015 (about 11% of all deaths). Three out of four stroke survivors suffer long term disability, as many cannot return to their prior employment or live independently. Eighty-seven percent of strokes are
Functions necessary for gait independence in patients with stroke: A study using decision tree
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Background: Little is known about the existence and impact of interactions among multiple impairments to gait independence. The purpose of this study is to reveal the interaction of physical functions and its impact on gait independence
Decision-tree model of treatment-seeking behaviors after detecting symptoms by Korean stroke patients.
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OBJECTIVE
This study was performed to develop and test a decision-tree model of treatment-seeking behaviors about when Korean patients visit a doctor after experiencing stroke symptoms.
METHODS
The study used methodological triangulation. The model was developed based on qualitative data collected
Tree and spline based association analysis of gene-gene interaction models for ischemic stroke.
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In the biology of complex disorders, such as atherothrombosis, interactions among genetic factors may play an important role, and theoretical considerations suggest that gene-gene interactions are quite common in such diseases. We used a nested case-control sample from the Physicians' Health Study,
Comparison between Decision Tree and Genetic Programming to distinguish healthy from stroke postural sway patterns.
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Maintaining balance is a motor task of crucial importance for humans to perform their daily activities safely and independently. Studies in the field of Artificial Intelligence have considered different classification methods in order to distinguish healthy subjects from patients with certain motor
[Study on the application of classification tree model in screening the risk factors of ischemic stroke].
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OBJECTIVE
To construct a prediction model for the risk of ischemic stroke (IS) by classification tree model, and evaluate its application value.
METHODS
By cluster sampling, 858 IS patients with perfect clinical data from January to December 2017 in the Affiliated Hospital of Guilin Medical College
Low birth weight and the developing vascular tree: a systematic review.
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Low birth weight (LBW) is a risk factor for hypertension, stroke and coronary heart disease in adults. Mechanisms underlying cardiovascular disease may therefore be initiated in early life. Studies to investigate the initiating events and emergence of vascular risk markers in infancy and childhood
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