1,698 Hits in 5.7 sec

Deep learning for small and big data in psychiatry

Georgia Koppe, Andreas Meyer-Lindenberg, Daniel Durstewitz
2020 Neuropsychopharmacology  
Here, we aim at giving a comprehensive overview on how we can yet use such models for prediction in psychiatry.  ...  Modern algorithms and approaches from machine learning, particularly deep learning, provide new hope to address these issues given their outstanding prediction performance in other disciplines.  ...  Similarly, one could use generative models like GANs to fill in missing values in multi-modal data sets, a common problem in psychiatry, rather than discarding an entire multivariate data point (see e.g  ... 
doi:10.1038/s41386-020-0767-z pmid:32668442 fatcat:c4aon35pf5c4ro3qikvw7qbgsi

Neuroimaging genomics in psychiatry—a translational approach

Mary S. Mufford, Dan J. Stein, Shareefa Dalvie, Nynke A. Groenewold, Paul M. Thompson, Neda Jahanshad
2017 Genome Medicine  
Notably, genomewide association studies (GWAS) of brain imaging in thousands of individuals around the world have led to a range of promising findings.  ...  Neuroimaging genomics is a relatively new field focused on integrating genomic and imaging data in order to investigate the mechanisms underlying brain phenotypes and neuropsychiatric disorders.  ...  Future work in imaging genomics will rely on further advances in neuroimaging technology, as well as on multi-modal approaches.  ... 
doi:10.1186/s13073-017-0496-z pmid:29179742 pmcid:PMC5704437 fatcat:dkp4b47r7za73owbdcbplfwe2e

The Project for Objective Measures Using Computational Psychiatry Technology (PROMPT): Rationale, Design, and Methodology

Taishiro Kishimoto, Akihiro Takamiya, Kuo-ching Liang, Kei Funaki, Takanori Fujita, Momoko Kitazawa, Michitaka Yoshimura, Yuki Tazawa, Toshiro Horigome, Yoko Eguchi, Toshiaki Kikuchi, Masayuki Tomita (+11 others)
2019 biorxiv/medrxiv  
A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/det [...]  ...  Methods: This is a multi-center prospective study.  ...  The following feature engineering approaches are used to construct features from the multi-modal data as input to the machine learning models for predicting a subject's depression/cognitive status and/  ... 
doi:10.1101/19013011 fatcat:xonp72qatjgonbqk6tir5yv3ga

The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology

Taishiro Kishimoto, Akihiro Takamiya, Kuo-ching Liang, Kei Funaki, Takanori Fujita, Momoko Kitazawa, Michitaka Yoshimura, Yuki Tazawa, Toshiro Horigome, Yoko Eguchi, Toshiaki Kikuchi, Masayuki Tomita (+11 others)
2020 Contemporary Clinical Trials Communications  
A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms.  ...  However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs.  ...  The following feature engineering approaches are used to construct features from the multi-modal data as input to the machine learning models for predicting a subject's depression/cognitive status and/  ... 
doi:10.1016/j.conctc.2020.100649 pmid:32913919 pmcid:PMC7473877 fatcat:4jbqhacbbzhkjmt4rgbyxg6zom

Training for child and adolescent psychiatry in the twenty-first century

Peter Deschamps, Johannes Hebebrand, Brian Jacobs, Paul Robertson, Dimitris C. Anagnostopoulos, Tobias Banaschewski, Sarah M. Birkle, Bernadka Dubicka, Bruno Falissard, Ioanna Giannopoulou, Pieter J. Hoekstra, Michael Kaess (+7 others)
2020 European Child and Adolescent Psychiatry  
We are a relatively young medical specialty and have found our roots in the past century in a combination of medicine (adult psychiatry and pediatrics), psychology and cooperation with various professionals  ...  At the same time, there has also been a tremendous increase in demand for child and adolescent mental health services.  ...  Perhaps initial psychotherapy interventions, or psychoeducation, may be amenable to machine-based approaches.  ... 
doi:10.1007/s00787-019-01467-6 pmid:31950371 pmcid:PMC6987048 fatcat:yxcak6a45rfitm5znhleronm6a

From multivariate methods to an AI ecosystem

Nils R. Winter, Micah Cearns, Scott R. Clark, Ramona Leenings, Udo Dannlowski, Bernhard T. Baune, Tim Hahn
2021 Molecular Psychiatry  
For example, at a recent international machine learning competition, participants sought to classify major depressive disorder (MDD) patients from healthy controls using structural Magnetic Resonance Imaging  ...  In the following, we will argue that the current drawbacks in psychiatry arise not primarily from a lack of methodological advancement and genuine clinical potential for AI in psychiatry, but from fundamental  ...  Acknowledgements This work was funded by the German Research Foundation (DFG grants HA7070/2-2, HA7070/3, HA7070/4 to TH) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty  ... 
doi:10.1038/s41380-021-01116-y pmid:33981009 pmcid:PMC8760040 fatcat:hspc2ldl5zblzhx3kcmzcqn5jm

Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment [article]

Linden Parkes, Theodore D. Satterthwaite, Danielle S. Bassett
2020 arXiv   pre-print
We provide a framework for tying these subfields together that draws on tools from machine learning and network science.  ...  Searching for biomarkers has been a chief pursuit of the field of psychiatry. Toward this end, studies have catalogued candidate resting-state biomarkers in nearly all forms of mental disorder.  ...  In particular, recent developments in machine learning have provided new tools for modeling, and detecting abnormalities in, neurodevelopment [8] .  ... 
arXiv:2006.04728v1 fatcat:rjuq2v3epneydkhkhir5xpcmju

Artificial Intelligence Approaches to Predicting and Detecting Cognitive Decline in Older Adults: A Conceptual Review

Sarah A. Graham, Ellen E. Lee, Dilip V. Jeste, Ryan Van Patten, Elizabeth W. Twamley, Camille Nebeker, Yasunori Yamada, Ho-Cheol Kim, Colin A. Depp
2019 Psychiatry Research  
AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease.  ...  For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing.  ...  Acknowledgments This study was supported, in part, by the National Institute of Mental Health T32 Geriatric Mental Health Program (grant MH019934 to DVJ [PI]), NIMH K23MH119375-01 (PI: EEL), the IBM Research  ... 
doi:10.1016/j.psychres.2019.112732 pmid:31978628 pmcid:PMC7081667 fatcat:gcfvge3wwrdt5kp6cv35iyijd4

ENIGMA and Global Neuroscience: A Decade of Large-Scale Studies of the Brain in Health and Disease Across More Than 40 Countries

Paul Thompson
2020 Biological Psychiatry  
This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying  ...  neuroscience, psychiatry, neurology, and genetics.  ...  Acknowledgements The work reported here was supported in part by many public and private agencies across the world. Individual authors' funding is listed in Supplementary Appendix B.  ... 
doi:10.1016/j.biopsych.2020.02.167 fatcat:rcamgpjlcvfgzikfleqbunef3u

The Utility of a Computerized Algorithm Based on a Multi-Domain Profile of Measures for the Diagnosis of Attention Deficit/Hyperactivity Disorder

Alessandro Crippa, Christian Salvatore, Erika Molteni, Maddalena Mauri, Antonio Salandi, Sara Trabattoni, Carlo Agostoni, Massimo Molteni, Maria Nobile, Isabella Castiglioni
2017 Frontiers in Psychiatry  
The present machine-learning classification approach might be helpful for supporting the clinical practice of diagnosing ADHD, even fostering a computer-aided diagnosis perspective.  ...  These preliminary findings show the feasibility and applicability of our machine-learning method in correctly identifying children with ADHD based on multidomain data.  ...  The authors are especially grateful to all the families of the children who took part in this study.  ... 
doi:10.3389/fpsyt.2017.00189 pmid:29042856 pmcid:PMC5632354 fatcat:wajuuyjuhrcsdal642rgockmhe

Sequential sampling models in computational psychiatry: Bayesian parameter estimation, model selection and classification [article]

Thomas V. Wiecki
2013 arXiv   pre-print
One particularly promising approach is the emerging field of computational psychiatry.  ...  I will then describe a set of methods that together form a toolbox of cognitive models to aid this research program.  ...  C Andrieu, N De Freitas, A Doucet, and MI Jordan. An introduction to MCMC for machine learning. Machine learning, 2003. URL CE Antoniak.  ... 
arXiv:1303.5616v1 fatcat:ojkzcnl3cnepxpzompbqptd2om

Research Review: Use of EEG biomarkers in child psychiatry research - current state and future directions

Sandra K. Loo, Agatha Lenartowicz, Scott Makeig
2015 Journal of Child Psychology and Psychiatry and Allied Disciplines  
To model complex relationships in the data, advanced statistical methods including machine learning, graph theory, discriminant functions, and logistic regression are also increasingly required.  ...  Recent studies have applied machine learning methods to combined ERP measures, with more success.  ... 
doi:10.1111/jcpp.12435 pmid:26099166 pmcid:PMC4689673 fatcat:xydpkg4wrbdgzp73iyadfr6fp4

Machine learning classification of ADHD and HC by multimodal serotonergic data

A. Kautzky, T. Vanicek, C. Philippe, G. S. Kranz, W. Wadsak, M. Mitterhauser, A. Hartmann, A. Hahn, M. Hacker, D. Rujescu, S. Kasper, R. Lanzenberger
2020 Translational Psychiatry  
Cortical and subcortical regions of interest (ROI) were defined and random forest (RF) machine learning was used for feature selection and classification in a five-fold cross-validation model with ten  ...  We propose a multivariate, genetic and positron emission tomography (PET) imaging classification model for ADHD and healthy controls (HC).  ...  Acknowledgements We are grateful to the technical and medical teams of the PET Centre Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional  ... 
doi:10.1038/s41398-020-0781-2 pmid:32265436 fatcat:lgpa5sa4ybhurg7xpaugx7vxtm

Prediction of Adolescent Subjective Well-Being: A Machine Learning Approach

Z Naixin, L Chuanxin, A Lin, C Zhixuan, R Decheng, Y Fan, Y Ruixue, J Lei, B Yan, G Zhenming, M Gaini, X Fei (+8 others)
2019 Mathews Journal of Psychiatry & Mental Health  
Methods: We used Gradient Boosting Classifier, an innovative yet validated machine learning approach to analyze data from 10 518 Chinese adolescents.  ...  Results: Top 20 happiness risks and protective factors were finally brought intothe predicting model.  ...  Acknowledgements including declarations We appreciate the contribution of the members participating in this study. This work was supported by the National Key  ... 
doi:10.30654/mjpmh.10021 fatcat:z4v2uxc5gzfdfes2xmlteomsvq

Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Abraham Nunes, Hugo G. Schnack, Christopher R. K. Ching, Ingrid Agartz, Theophilus N. Akudjedu, Martin Alda, Dag Alnæs, Silvia Alonso-Lana, Jochen Bauer, Bernhard T. Baune, Erlend Bøen, Caterina del Mar Bonnin (+62 others)
2018 Molecular Psychiatry  
Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use.  ...  Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD.  ...  The first was a meta-analysis of diagnostic accuracy from site-level analyses, referred to as meta-analysis. This models the typical method of analyzing data in a multi-site collaboration [11, 14] .  ... 
doi:10.1038/s41380-018-0228-9 pmid:30171211 pmcid:PMC7473838 fatcat:rlm5npxfjvcsrbek2txv6jtkfa
« Previous Showing results 1 — 15 out of 1,698 results