Identifying suicide attempts, ideation, and non-ideation in major depressive disorder from structural MRI data using deep learning.

[1]  Lalasa Mukku,et al.  A machine learning model to predict suicidal tendencies in students. , 2022, Asian journal of psychiatry.

[2]  M. H. M. Noor,et al.  An explainable predictive model for suicide attempt risk using an ensemble learning and Shapley Additive Explanations (SHAP) approach. , 2022, Asian journal of psychiatry.

[3]  R. Gupta,et al.  Artificial intelligence and Psychiatry: An overview , 2022, Asian journal of psychiatry.

[4]  A. Baminiwatta.  Global trends of machine learning applications in psychiatric research over 30 years: A bibliometric analysis. , 2021, Asian journal of psychiatry.

[5]  Jingliang Cheng,et al.  Reduced Brain Gray Matter Volume in Patients With First-Episode Major Depressive Disorder: A Quantitative Meta-Analysis , 2021, Frontiers in Psychiatry.

[6]  Dick J. Veltman,et al.  Associations between depression, lifestyle and brain structure: A longitudinal MRI study , 2021, NeuroImage.

[7]  A. Greenshaw,et al.  Identification of suicidality in adolescent major depressive disorder patients using sMRI: A machine learning approach. , 2020, Journal of affective disorders.

[8]  Bin Liao,et al.  Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder , 2020, Comput. Math. Methods Medicine.

[9]  Man Teng Cheok,et al.  An Autoencoder and Machine Learning Model to Predict Suicidal Ideation with Brain Structural Imaging , 2020, Journal of clinical medicine.

[10]  Runa Bhaumik,et al.  Innate immunity in the postmortem brain of depressed and suicide subjects: Role of Toll-like receptors , 2019, Brain, Behavior, and Immunity.

[11]  J. Sweeney,et al.  Brain structure alterations in depression: Psychoradiological evidence , 2018, CNS neuroscience & therapeutics.

[12]  J. Ribeiro,et al.  Depression and hopelessness as risk factors for suicide ideation, attempts and death: meta-analysis of longitudinal studies , 2018, British Journal of Psychiatry.

[13]  A. Serretti,et al.  Major Depression and the Degree of Suicidality: Results of the European Group for the Study of Resistant Depression (GSRD) , 2018, The international journal of neuropsychopharmacology.

[14]  Jian Pei,et al.  Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution , 2018, KDD.

[15]  Yang Yang,et al.  Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features , 2017, Oncotarget.

[16]  D. Mohr,et al.  Major depressive disorder , 2016, Nature Reviews Disease Primers.

[17]  Colm G. Connolly,et al.  Fusiform Gyrus Dysfunction is Associated with Perceptual Processing Efficiency to Emotional Faces in Adolescent Depression: A Model-Based Approach , 2016, Front. Psychol..

[18]  V. Diwadkar,et al.  Impulsivity, aggression and brain structure in high and low lethality suicide attempters with borderline personality disorder , 2014, Psychiatry Research: Neuroimaging.

[19]  Keith Hawton,et al.  Risk factors for suicide in individuals with depression: a systematic review. , 2013, Journal of affective disorders.

[20]  V. Diwadkar,et al.  Structural brain abnormalities and suicidal behavior in borderline personality disorder. , 2012, Journal of psychiatric research.

[21]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[22]  M. Hamilton.  A RATING SCALE FOR DEPRESSION , 1960, Journal of neurology, neurosurgery, and psychiatry.