相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Single and Cross-Disorder Detection for Autism and Schizophrenia
Aleksander Wawer et al.
COGNITIVE COMPUTATION (2022)
Development and validation of a machine learning-based postpartum depression prediction model: A nationwide cohort study
Eldar Hochman et al.
DEPRESSION AND ANXIETY (2021)
Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women
Yiye Zhang et al.
JOURNAL OF AFFECTIVE DISORDERS (2021)
Can machine learning be useful as a screening tool for depression in primary care?
Erito Marques de Souza Filho et al.
JOURNAL OF PSYCHIATRIC RESEARCH (2021)
Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence
Matthew D. Nemesure et al.
SCIENTIFIC REPORTS (2021)
A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data
Jakub Tomasik et al.
TRANSLATIONAL PSYCHIATRY (2021)
Use of machine learning approach to predict depression in the elderly in China: A longitudinal study
Dai Su et al.
JOURNAL OF AFFECTIVE DISORDERS (2021)
Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness
Juliet B. Edgcomb et al.
MEDICAL CARE (2021)
A textual-based featuring approach for depression detection using machine learning classifiers and social media texts
Raymond Chiong et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2021)
Machine learning-based diagnosis support system for differentiating between clinical anxiety and depression disorders
Thalia Richter et al.
JOURNAL OF PSYCHIATRIC RESEARCH (2021)
Personalized cognitive training: Protocol for individual-level meta-analysis implementing machine learning methods
Reut Shani et al.
JOURNAL OF PSYCHIATRIC RESEARCH (2021)
Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study
Kennedy Opoku Asare et al.
JMIR MHEALTH AND UHEALTH (2021)
Toward Personalized Psychotherapy: The Importance of the Trait-Like/State-Like Distinction for Understanding Therapeutic Change
Sigal Zilcha-Mano
AMERICAN PSYCHOLOGIST (2021)
Automatic mental health identification method based on natural gait pattern
Beibei Miao et al.
PSYCH JOURNAL (2021)
Bridges over Troubled Waters: Mapping the Interplay Between Anxiety, Depression and Stress Through Network Analysis of the DASS-21
Nathan van den Bergh et al.
COGNITIVE THERAPY AND RESEARCH (2021)
Differential biomarker signatures in unipolar and bipolar depression: A machine learning approach
Bianca Wollenhaupt-Aguiar et al.
AUSTRALIAN AND NEW ZEALAND JOURNAL OF PSYCHIATRY (2020)
Vocal pattern detection of depression among older adults
Marianne Smith et al.
INTERNATIONAL JOURNAL OF MENTAL HEALTH NURSING (2020)
Depression screening using mobile phone usage metadata: a machine learning approach
Rouzbeh Razavi et al.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2020)
Machine Learning for Differential Diagnosis Between Clinical Conditions With Social Difficulty: Autism Spectrum Disorder, Early Psychosis, and Social Anxiety Disorder
Eleni A. Demetriou et al.
FRONTIERS IN PSYCHIATRY (2020)
Comorbidity between depression and anxiety: assessing the role of bridge mental states in dynamic psychological networks
Robin N. Groen et al.
BMC MEDICINE (2020)
Using machine learning-based analysis for behavioral differentiation between anxiety and depression
Thalia Richter et al.
SCIENTIFIC REPORTS (2020)
Improving Diagnosis of Depression With XGBOOST Machine Learning Model and a Large Biomarkers Dutch Dataset (n=11,081)
Amita Sharma et al.
FRONTIERS IN BIG DATA (2020)
Evaluating depression with multimodal wristband-type wearable device: screening and assessing patient severity utilizing machine-learning
Yuuki Tazawa et al.
HELIYON (2020)
Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcare
Christopher M. Hatton et al.
JOURNAL OF AFFECTIVE DISORDERS (2019)
Machine learning in mental health: a scoping review of methods and applications
Adrian B. R. Shatte et al.
PSYCHOLOGICAL MEDICINE (2019)
Can Machine Learning Approaches Lead Toward Personalized Cognitive Training?
Reut Shani et al.
FRONTIERS IN BEHAVIORAL NEUROSCIENCE (2019)
Identifying depression in the National Health and Nutrition Examination Survey data using a deep learning algorithm
Jihoon Oh et al.
JOURNAL OF AFFECTIVE DISORDERS (2019)
Machine learning algorithm validation with a limited sample size
Andrius Vabalas et al.
PLOS ONE (2019)
Giving Voice to Vulnerable Children: Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood
Ellen W. McGinnis et al.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2019)
Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone
Heejung Kim et al.
JMIR MHEALTH AND UHEALTH (2019)
Individually tailored internet treatment in routine care: A feasibility study
Martin Kraepelien et al.
INTERNET INTERVENTIONS-THE APPLICATION OF INFORMATION TECHNOLOGY IN MENTAL AND BEHAVIOURAL HEALTH (2019)
Detecting Depression Using a Framework Combining Deep Multimodal Neural Networks With a Purpose-Built Automated Evaluation
Ezekiel Victor et al.
PSYCHOLOGICAL ASSESSMENT (2019)
Individually tailored vs. standardized substrate modification during radiofrequency catheter ablation for atrial fibrillation: a randomized study
Simon Kircher et al.
EUROPACE (2018)
Cross-validation failure: Small sample sizes lead to large error bars
Gael Varoquaux
NEUROIMAGE (2018)
Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning
Joao Ricardo Sato et al.
WORLD JOURNAL OF BIOLOGICAL PSYCHIATRY (2018)
Automated Depression Diagnosis Based on Deep Networks to Encode Facial Appearance and Dynamics
Yu Zhu et al.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING (2018)
Pattern recognition of magnetic resonance imaging-based gray matter volume measurements classifies bipolar disorder and major depressive disorder
Harry Rubin-Falcone et al.
JOURNAL OF AFFECTIVE DISORDERS (2018)
In Schizophrenia, Depression, Anxiety, and Physiosomatic Symptoms Are Strongly Related to Psychotic Symptoms and Excitation, Impairments in Episodic Memory, and Increased Production of Neurotoxic Tryptophan Catabolites: a Multivariate and Machine Learning Study
Buranee Kanchanatawan et al.
NEUROTOXICITY RESEARCH (2018)
Machine learning in major depression: From classification to treatment outcome prediction
Shuang Gao et al.
CNS NEUROSCIENCE & THERAPEUTICS (2018)
Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017
Spencer L. G. James et al.
LANCET (2018)
Depression detection from social network data using machine learning techniques
Md. Rafiqul Islam et al.
HEALTH INFORMATION SCIENCE AND SYSTEMS (2018)
Machine Learning for Precision Psychiatry: Opportunities and Challenges
Danilo Bzdok et al.
BIOLOGICAL PSYCHIATRY-COGNITIVE NEUROSCIENCE AND NEUROIMAGING (2018)
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Mohammad R. Arbabshirani et al.
NEUROIMAGE (2017)
Diagnosis of major depressive disorder by combining multimodal information from heart rate dynamics and serum proteomics using machine-learning algorithm
Eun Young Kim et al.
PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY (2017)
Instagram photos reveal predictive markers of depression
Andrew G. Reece et al.
EPJ DATA SCIENCE (2017)
Forecasting the onset and course of mental illness with Twitter data
Andrew G. Reece et al.
SCIENTIFIC REPORTS (2017)
Predicting anxiety and depression in elderly patients using machine learning technology
Arkaprabha Sau et al.
HEALTHCARE TECHNOLOGY LETTERS (2017)
Detecting depression and mental illness on social media: an integrative review
Sharath Chandra Guntuku et al.
CURRENT OPINION IN BEHAVIORAL SCIENCES (2017)
Studying depression using imaging and machine learning methods
Meenal J. Patel et al.
NEUROIMAGE-CLINICAL (2016)
Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression
Joanna F. Dipnall et al.
PLOS ONE (2016)
Cross-trial prediction of treatment outcome in depression: a machine learning approach
Adam Mourad Chekroud et al.
LANCET PSYCHIATRY (2016)
Sparse network-based models for patient classification using fMRI
Maria J. Rosa et al.
NEUROIMAGE (2015)
Serum proteomic profiling of major depressive disorder
M. Bot et al.
TRANSLATIONAL PSYCHIATRY (2015)
Anxiety disorders are associated with reduced heart rate variability: a meta-analysis
John A. Chalmers et al.
FRONTIERS IN PSYCHIATRY (2014)
Multimodal assistive technologies for depression diagnosis and monitoring
Jyoti Joshi et al.
JOURNAL ON MULTIMODAL USER INTERFACES (2013)
A Systematic Review of Comparative Efficacy of Treatments and Controls for Depression
Arif Khan et al.
PLOS ONE (2012)
A Meta-Analysis of the Effect of Cognitive Bias Modification on Anxiety and Depression
Lauren S. Hallion et al.
PSYCHOLOGICAL BULLETIN (2011)
Attention Bias Modification Treatment: A Meta-Analysis Toward the Establishment of Novel Treatment for Anxiety
Yuko Hakamata et al.
BIOLOGICAL PSYCHIATRY (2010)
Self-report bias and underreporting of depression on the BDI-II
M Hunt et al.
JOURNAL OF PERSONALITY ASSESSMENT (2003)
Understanding self-report bias in organizational behavior research
SI Donaldson et al.
JOURNAL OF BUSINESS AND PSYCHOLOGY (2002)
Serotonin reuptake inhibition: An update on current research strategies
D Spinks et al.
CURRENT MEDICINAL CHEMISTRY (2002)
Multi-dimensional criteria for the diagnosis of depression
J Angst et al.
JOURNAL OF AFFECTIVE DISORDERS (2001)
分享论文
