Machine Learning for Precision Psychiatry: Opportunities and Challenges.

[1]  北村 聖.  "The New England Journal of Medicine". , 1962, British medical journal.

[2]  A. Feinstein,et al.  Problems of spectrum and bias in evaluating the efficacy of diagnostic tests. , 1978, The New England journal of medicine.

[3]  J. Friedman,et al.  Predicting Multivariate Responses in Multiple Linear Regression , 1997 .

[4]  Rich Caruana,et al.  Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.

[5]  Farid Neema,et al.  Data sharing , 1998 .

[6]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .

[7]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001, Statistical Science.

[8]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[9]  M. De Hert,et al.  Cost of disorders of the brain in Europe. , 2006, European journal of neurology.

[10]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[11]  D. Kupfer,et al.  Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. , 2006, The American journal of psychiatry.

[12]  S. Hyman.  Can neuroscience be integrated into the DSM-V? , 2007, Nature Reviews Neuroscience.

[13]  Tom M. Mitchell,et al.  Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.

[14]  N. Kriegeskorte,et al.  Revealing representational content with pattern-information fMRI--an introductory guide. , 2009, Social cognitive and affective neuroscience.

[15]  T. Insel,et al.  Wesleyan University From the SelectedWorks of Charles A . Sanislow , Ph . D . 2010 Research Domain Criteria ( RDoC ) : Toward a New Classification Framework for Research on Mental Disorders , 2018 .

[16]  Galit Shmueli,et al.  To Explain or To Predict? , 2010 .

[17]  Mark A. Smith,et al.  Challenges and opportunities for drug discovery in psychiatric disorders: the drug hunters' perspective. , 2010, The international journal of neuropsychopharmacology.

[18]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[19]  R. Perlis,et al.  Translating biomarkers to clinical practice , 2011, Molecular Psychiatry.

[20]  J. Os,et al.  Cost of disorders of the brain in Europe 2010 , 2011, European Neuropsychopharmacology.

[21]  Pedro M. Domingos.  A few useful things to know about machine learning , 2012, Commun. ACM.

[22]  Alice T. Sawyer,et al.  The Efficacy of Cognitive Behavioral Therapy: A Review of Meta-analyses , 2012, Cognitive Therapy and Research.

[23]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[24]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  M. Daly,et al.  Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis , 2013, The Lancet.

[26]  T. Insel,et al.  Toward the future of psychiatric diagnosis: the seven pillars of RDoC , 2013, BMC Medicine.

[27]  Krzysztof J. Gorgolewski,et al.  Making big data open: data sharing in neuroimaging , 2014, Nature Neuroscience.

[28]  Shai Ben-David,et al.  Understanding Machine Learning: From Theory to Algorithms , 2014 .

[29]  J. Haynes.  A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives , 2015, Neuron.

[30]  T. Insel,et al.  Brain disorders? Precisely , 2015, Science.

[31]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[32]  Satrajit S. Ghosh,et al.  Prediction as a Humanitarian and Pragmatic Contribution from Human Cognitive Neuroscience , 2015, Neuron.

[33]  C. Moskowitz.  Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. , 2016 .

[34]  Cathy O'Neil,et al.  Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy , 2016, Vikalpa: The Journal for Decision Makers.

[35]  M. Frank,et al.  Computational psychiatry as a bridge from neuroscience to clinical applications , 2016, Nature Neuroscience.

[36]  J. van Os,et al.  “Schizophrenia” does not exist , 2016, BMJ : British Medical Journal.

[37]  R. Sperling,et al.  Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease , 2016, Proceedings of the National Academy of Sciences.

[38]  Daniel R Weinberger,et al.  Finding the Elusive Psychiatric "Lesion" With 21st-Century Neuroanatomy: A Note of Caution. , 2015, The American journal of psychiatry.

[39]  N. Lazar,et al.  The ASA Statement on p-Values: Context, Process, and Purpose , 2016 .

[40]  Charles Reynolds,et al.  Tech giants enter mental health , 2016, World psychiatry : official journal of the World Psychiatric Association.

[41]  Marcia McNutt,et al.  Data sharing , 2016, Science.

[42]  Satrajit S. Ghosh,et al.  The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments , 2016, Scientific Data.

[43]  Andrew T. Drysdale,et al.  Resting-state connectivity biomarkers define neurophysiological subtypes of depression , 2016, Nature Medicine.

[44]  Satrajit S. Ghosh,et al.  BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods , 2016, bioRxiv.

[45]  Karl J. Friston,et al.  Computational neuroimaging strategies for single patient predictions , 2017, NeuroImage.

[46]  Danilo Bzdok,et al.  Classical Statistics and Statistical Learning in Imaging Neuroscience , 2016, Front. Neurosci..

[47]  M. Paulus,et al.  Neural Predictors of Initiating Alcohol Use During Adolescence. , 2017, The American journal of psychiatry.

[48]  Vince D. Calhoun,et al.  Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls , 2017, NeuroImage.

[49]  Personalized Medicine in Psychiatry: Back to the Future , 2017 .

[50]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[51]  B. T. Thomas Yeo,et al.  Inference in the age of big data: Future perspectives on neuroscience , 2017, NeuroImage.

[52]  Luke J. Chang,et al.  Building better biomarkers: brain models in translational neuroimaging , 2017, Nature Neuroscience.

[53]  H. Grabe,et al.  OTTO: a new strategy to extract mental disease-relevant combinations of GWAS hits from individuals , 2016, Molecular Psychiatry.

[54]  Gaël Varoquaux,et al.  Cross-validation failure: Small sample sizes lead to large error bars , 2017, NeuroImage.

[55]  S. Rahimifard,et al.  Unlocking the Potential of the Internet of Things to Improve Resource Efficiency in Food Supply Chains , 2017, Innovative Approaches and Applications for Sustainable Rural Development.