Results 61 to 70 of about 16,649 (216)
NeuralNetTools: Visualization and Analysis Tools for Neural Networks
Journal of Statistical Software, 2018 Supervised neural networks have been applied as a machine learning technique to identify and predict emergent patterns among multiple variables. A common criticism of these methods is the inability to characterize relationships among variables from a ...
Marcus W. Beck
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Importance of Substrate Variability to Enzyme Polymorphism
Nature New Biology, 1973 Uploaded by Plazi for TaxoDros. We do not have abstracts.
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Introduction The use of herbal medical preparation (HMP) is rising among pediatric oncology patients, often to manage treatment‐related symptoms. Their effectiveness remains uncertain, and the risk of herb–drug interactions is underestimated.
Orianne Mahot +6 more
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Variable Importance Without Impossible Data
Annual Review of Statistics and Its ApplicationThe most popular methods for measuring importance of the variables in a black-box prediction algorithm make use of synthetic inputs that combine predictor variables from multiple observations. These inputs can be unlikely, physically impossible, or even logically impossible.
Masayoshi Mase +2 more
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Unbiased variable importance for random forests [PDF]
Communications in Statistics - Theory and Methods, 2020 The default variable-importance measure in random Forests, Gini importance, has been shown to suffer from the bias of the underlying Gini-gain splitting criterion. While the alternative permutation importance is generally accepted as a reliable measure of variable importance, it is also computationally demanding and suffers from other shortcomings.
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Variable Importance and Prediction Methods for Longitudinal Problems with Missing Variables [PDF]
PLOS ONE, 2015 We present prediction and variable importance (VIM) methods for longitudinal data sets containing continuous and binary exposures subject to missingness. We demonstrate the use of these methods for prognosis of medical outcomes of severe trauma patients, a field in which current medical practice involves rules of thumb and scoring methods that only use
Díaz, Iván +3 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Pediatric radiation therapy presents unique challenges compared to adult treatments, including those of immobilization, potential need for sedation, and the critical importance of accurate, reproducible positioning. Additionally, heightened attention to imaging doses is necessary to minimize long‐term toxicity in survivors.
Parham Alaei +17 more
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Personalized Zebrafish Models for Fusion‐Positive Pediatric Sarcomas
Pediatric Blood &Cancer, EarlyView.ABSTRACT Clinical sequencing efforts have revolutionized our approaches to categorizing pediatric cancers in real time. This has dramatically improved our ability to profile pediatric tumors, identify actionable vulnerabilities, and influence clinical care.
Lisa H. Hall +2 more
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Seminal Quality Prediction Using Clustering-Based Decision Forests
Algorithms, 2014 Prediction of seminal quality with statistical learning tools is an emerging methodology in decision support systems in biomedical engineering and is very useful in early diagnosis of seminal patients and selection of semen donors candidates. However, as
Hong Wang, Qingsong Xu, Lifeng Zhou
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Variable Importance Matching for Causal Inference
, 2023 Our goal is to produce methods for observational causal inference that are auditable, easy to troubleshoot, accurate for treatment effect estimation, and scalable to high-dimensional data. We describe a general framework called Model-to-Match that achieves these goals by (i) learning a distance metric via outcome modeling, (ii) creating matched groups ...
Quinn Lanners +4 more
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