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Analysis and predictive modeling of asthma phenotypes
Allan R. Brasier, Hyunsu Ju
Internal Medicine
Research output
:
Chapter in Book/Report/Conference proceeding
›
Chapter
5
Scopus citations
Overview
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Dive into the research topics of 'Analysis and predictive modeling of asthma phenotypes'. Together they form a unique fingerprint.
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Keyphrases
Predictive Models
100%
Asthma Phenotypes
100%
Analysis Modeling
100%
Predictive Modeling
100%
Pathophysiology
50%
Lack of Information
50%
Protein-coding Genes
50%
Asthma
50%
Clinical Outcomes
50%
Asthma Management
50%
Clinical Management
50%
Receiver Operating Characteristic Curve
50%
Outcome Prediction
50%
Random Forest Regression
50%
Nonparametric Regression
50%
Supervised Classification
50%
Logistic Regression Classification
50%
Multidimensional Measurement
50%
Generalized Additive Model
50%
Classification and Regression Tree
50%
Spline Model
50%
Regression Splines
50%
Informative Features
50%
Collinearity
50%
Evaluation Model
50%
Phenotypic Measurement
50%
Biochemical Measurements
50%
Subtle Differences
50%
Diagnostic Precision
50%
Physiological Assessment
50%
Molecular Classification
50%
Model Performance
50%
Clinical Assessment
50%
High-dimensional Data
50%
Information Content
50%
Biochemical Analytes
50%
Curse of Dimensionality
50%
Mathematics
Predictive Model
100%
Predictive Modeling
100%
Information Content
50%
Regression tree
50%
Performance Model
50%
Logistic Regression
50%
Collinearity
50%
Spline
50%
Curse of Dimensionality
50%
Dimensional Data
50%
Generalized Additive Model
50%
Computer Science
Predictive Model
100%
Clinical Outcome
50%
Molecular Classification
50%
Random Decision Forest
50%
High Dimensional Data
50%
Evaluation Models
50%
Logistic Regression
50%
Performance Model
50%
Supervised Classification
50%
Information Content
50%
Regression Tree
50%
Biochemistry, Genetics and Molecular Biology
Metabolite
100%
Random Forest
100%