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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
Asthma Phenotypes
100%
Predictive Models
100%
Analysis Modeling
100%
Predictive Modeling
100%
Clinical Outcome
50%
Asthma
50%
Pathophysiology
50%
Protein-coding Genes
50%
Asthma Management
50%
Lack of Information
50%
Clinical Management
50%
Receiver Operating Characteristic Curve
50%
Clinical Assessment
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%
Diagnostic Precision
50%
Informative Features
50%
Collinearity
50%
Evaluation Model
50%
Biochemical Measurements
50%
Subtle Differences
50%
Physiological Assessment
50%
Molecular Classification
50%
Model Performance
50%
High-dimensional Data
50%
Information Content
50%
Biochemical Analytes
50%
Curse of Dimensionality
50%
INIS
asthma
100%
phenotype
100%
classification
100%
modeling
100%
data
66%
patients
33%
proteins
33%
management
33%
genes
33%
applications
33%
levels
33%
evaluation
33%
information
33%
assessments
33%
performance
33%
curves
33%
additives
33%
prediction
33%
forests
33%
metabolites
33%
randomness
33%
precision
33%
Mathematics
Modeling
100%
Predictive Model
100%
Predictive Modeling
100%
Phenotype
100%
Precision
50%
Prediction
50%
Performance Model
50%
Logistic Regression
50%
Spline
50%
Information Content
50%
Regression tree
50%
Collinearity
50%
Curse of Dimensionality
50%
Curve
50%
Dimensional Data
50%
Generalized Additive Model
50%
Computer Science
Modeling
100%
Predictive Model
66%
Application
33%
Model
33%
Clinical Outcome
33%
Molecular Classification
33%
Random Decision Forest
33%
High Dimensional Data
33%
Evaluation Models
33%
Logistic Regression
33%
Splines
33%
Precision
33%
Performance Model
33%
Supervised Classification
33%
Information Content
33%
Regression Tree
33%
Biochemistry, Genetics and Molecular Biology
Phenotype
100%
Classification
100%
Protein
33%
Gene
33%
Metabolite
33%
Tree
33%
Random Forest
33%