Machine Learning for Precision Psychiatry: Opportunities and Challenges

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

Volumn 3, Issue 3, 2018, Pages 223-230

  Bzdok, Danilo ^a,b,d   Meyer Lindenberg, Andreas ^c  

^a RWTH AACHEN UNIVERSITY   (Germany)

^b JARA BRAIN   (Germany)

^c UNIVERSITY OF HEIDELBERG   (Germany)

^d INRIA   (France)

Author keywords

Artificial intelligence; Endophenotypes; Machine learning; Null hypothesis testing; Personalized medicine; Predictive analytics; Research Domain Criteria (RDoC); Single subject prediction

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; DIAGNOSTIC AND STATISTICAL MANUAL OF MENTAL DISORDERS; ENDOPHENOTYPE; EVIDENCE BASED PRACTICE; HUMAN; INFORMATION PROCESSING; INTERNATIONAL CLASSIFICATION OF DISEASES; MACHINE LEARNING; MENTAL DISEASE; MENTAL PATIENT; PERSONALIZED MEDICINE; PREDICTION; PRIORITY JOURNAL; PSYCHIATRY; REVIEW; SUPERVISED MACHINE LEARNING; SUPPORT VECTOR MACHINE; TREATMENT OUTCOME; UNSUPERVISED MACHINE LEARNING; VALIDATION PROCESS; PROCEDURES;

ENDOPHENOTYPES; HUMANS; MACHINE LEARNING; MENTAL DISORDERS; PRECISION MEDICINE; PSYCHIATRY;

EID: 85041104529     PISSN: 24519022     EISSN: 24519030     Source Type: Journal    
DOI: 10.1016/j.bpsc.2017.11.007     Document Type: Review

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