Predicting Methylphenidate Response in ADHD Using Machine Learning Approaches.

Journal: The international journal of neuropsychopharmacology

PMID: 25964505

Abstract

BACKGROUND: There are no objective, biological markers that can robustly predict methylphenidate response in attention deficit hyperactivity disorder. This study aimed to examine whether applying machine learning approaches to pretreatment demographic, clinical questionnaire, environmental, neuropsychological, neuroimaging, and genetic information can predict therapeutic response following methylphenidate administration.

Authors

Jae-Won Kim

Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea (Dr Kim); Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA (Drs Sharma and Ryan).
Vinod Sharma

Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea (Dr Kim); Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA (Drs Sharma and Ryan).
Neal D Ryan

Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea (Dr Kim); Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA (Drs Sharma and Ryan). ryannd@upmc.edu.

Keywords

Age Factors Attention Deficit Disorder with Hyperactivity Body Weight Brain Central Nervous System Stimulants Child Cotinine Drug Therapy, Computer-Assisted Female Humans Lead Magnetic Resonance Imaging Male Methylphenidate Norepinephrine Plasma Membrane Transport Proteins Polymorphism, Genetic Prognosis Receptors, Adrenergic, alpha-2 Rest Stroop Test Support Vector Machine Treatment Outcome

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View on PubMed Access via DOI PubMed (25964505)

Predicting Methylphenidate Response in ADHD Using Machine Learning Approaches.

Abstract

Authors

Keywords

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