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Learning optimal biomarker-guided treatment policy for chronic disorders
Bin Yang
, Xingche Guo
,
Ji Meng Loh
, Qinxia Wang
, Yuanjia Wang
Mathematical Sciences
Research output
:
Contribution to journal
›
Article
›
peer-review
4
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Scopus citations
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Dive into the research topics of 'Learning optimal biomarker-guided treatment policy for chronic disorders'. Together they form a unique fingerprint.
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Keyphrases
Electroencephalogram
100%
Treatment Strategy
100%
Chronic Disorders
100%
Biomarker-guided Treatment
100%
Response Rate
50%
Average Treatment Effect
50%
Resting State
25%
Brain Activity
25%
Simulation Study
25%
Efficiency Improvement
25%
Learning Algorithm
25%
Theta Frequency
25%
Decision Tree
25%
Feature Extraction
25%
Treatment Effect
25%
Strong Signals
25%
Major Depressive Disorder
25%
Robust Techniques
25%
Q-learning
25%
Antidepressants
25%
Low Response
25%
Alpha Power
25%
Treatment Assignment
25%
Integrated pipeline
25%
Preprocessing pipeline
25%
Effect Modifier
25%
Randomized Controlled Clinical Trial
25%
Conditional Average Treatment Effect
25%
EMBARC
25%
Optimal Depth
25%
Outcome-based Learning
25%
Doubly Robust
25%
Electroencephalogram Signals
25%
Efficient Policy Learning
25%
Noninvasive Measures
25%
Causal Forest
25%
Medicine and Dentistry
Biological Marker
100%
Electroencephalogram
100%
Chronic Disorder
100%
Treatment Effect
83%
Controlled Clinical Trial
16%
Major Depressive Episode
16%
Antidepressant
16%
Social Sciences
Biological Marker
100%
Chronic Disorder
100%
Decision Tree
50%
Clinical Trial
50%
Depression
50%
Psychology
Chronic Disorder
100%
Electroencephalogram
100%
Resting-State
16%
Learning Algorithm
16%
Clinical Trial
16%
Major Depressive Disorder
16%
Major Depression
16%
Major Depressive Episode
16%
Neuroscience
Electroencephalogram
100%
Biological Marker
100%
Major Depressive Disorder
16%
Antidepressant
16%
Theta Frequency
16%
Biochemistry, Genetics and Molecular Biology
Electroencephalogram
100%
Decision Trees
16%
Controlled Clinical Trial
16%