Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using a support vector machine classifier

Keyphrases

• Bipolar Disorder 100%
• Classification Scheme 16%
• Death Rate 16%
• Dentate Gyrus 16%
• Diagnostic Algorithm 16%
• Electrophysiological Characteristics 16%
• Electrophysiological Recordings 16%
• Granule Neurons 16%
• High Morbidity 16%
• Information Theory 100%
• Lithium 50%
• Lithium Response 100%
• Lithium Treatment 33%
• Modern Information 16%
• Mood Disorders 16%
• Mood Stabilizers 16%
• Morbidity Rate 16%
• Patient Response 16%
• Patient-derived 16%
• Random Forest Classifier 16%
• Statistical Tools 16%
• Support Vector Machine 16%
• Support Vector Machine Classifier 100%

Neuroscience

• Bipolar Disorder 100%
• Dentate Gyrus 16%
• Mood Disorder 16%
• Mood Stabilizer 16%
• Support Vector Machine 100%

Pharmacology, Toxicology and Pharmaceutical Science

• Bipolar Disorder 100%
• Clinical Study 16%
• Diseases 16%
• Mood Disorder 16%
• Mood Stabilizer 16%
• Mortality Rate 16%