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For: Hong S, Liu YS, Cao B, Cao J, Ai M, Chen J, Greenshaw A, Kuang L. Identification of suicidality in adolescent major depressive disorder patients using sMRI: A machine learning approach. J Affect Disord 2021;280:72-6. [PMID: 33202340 DOI: 10.1016/j.jad.2020.10.077] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
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1 Balcombe L, De Leo D. The Potential Impact of Adjunct Digital Tools and Technology to Help Distressed and Suicidal Men: An Integrative Review. Front Psychol 2021;12:796371. [PMID: 35058855 DOI: 10.3389/fpsyg.2021.796371] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Yang Z, Jian L, Qiu H, Zhang C, Cheng S, Ji J, Li T, Wang Y, Li J, Li K. Understanding complex functional wiring patterns in major depressive disorder through brain functional connectome. Transl Psychiatry 2021;11:526. [PMID: 34645783 DOI: 10.1038/s41398-021-01646-7] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
3 Bhadra S, Kumar CJ. An insight into diagnosis of depression using machine learning techniques: a systematic review. Curr Med Res Opin 2022;:1-62. [PMID: 35129401 DOI: 10.1080/03007995.2022.2038487] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Moller CI, Davey CG, Badcock PB, Wrobel AL, Cao A, Murrihy S, Sharmin S, Cotton SM. Correlates of suicidality in young people with depressive disorders: A systematic review. Aust N Z J Psychiatry 2022;:48674221086498. [PMID: 35362327 DOI: 10.1177/00048674221086498] [Reference Citation Analysis]
5 Zhang Z, Li G, Xu Y, Tang X. Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review. Diagnostics (Basel) 2021;11:1402. [PMID: 34441336 DOI: 10.3390/diagnostics11081402] [Reference Citation Analysis]
6 Liang S, Zhang J, Zhao Q, Wilson A, Huang J, Liu Y, Shi X, Sha S, Wang Y, Zhang L. Incidence Trends and Risk Prediction Nomogram for Suicidal Attempts in Patients With Major Depressive Disorder. Front Psychiatry 2021;12:644038. [PMID: 34248696 DOI: 10.3389/fpsyt.2021.644038] [Reference Citation Analysis]
7 Marchionatti LE, Passos IC, Kapczinski F. Adding science to the art of suicide prevention. J bras psiquiatr 2021;70:191-2. [DOI: 10.1590/0047-2085000000340] [Reference Citation Analysis]
8 Li W, Wang C, Lan X, Fu L, Zhang F, Ye Y, Liu H, Wu K, Lao G, Chen J, Li G, Zhou Y, Ning Y. Aberrant Dynamic Functional Connectivity of Posterior Cingulate Cortex Subregions in Major Depressive Disorder With Suicidal Ideation. Front Neurosci 2022;16:937145. [DOI: 10.3389/fnins.2022.937145] [Reference Citation Analysis]
9 Overs BJ, Roberts G, Ridgway K, Toma C, Hadzi-Pavlovic D, Wilcox HC, Hulvershorn LA, Nurnberger JI, Schofield PR, Mitchell PB, Fullerton JM. Effects of polygenic risk for suicide attempt and risky behavior on brain structure in young people with familial risk of bipolar disorder. Am J Med Genet B Neuropsychiatr Genet 2021. [PMID: 34726322 DOI: 10.1002/ajmg.b.32879] [Reference Citation Analysis]
10 Passos IC. Could an algorithm help prevent suicides? J Affect Disord 2021;291:252-3. [PMID: 34052747 DOI: 10.1016/j.jad.2021.05.016] [Reference Citation Analysis]
11 Hao Z, Li H, Ouyang L, Sun F, Wen X, Wang X. Pain avoidance and functional connectivity between insula and amygdala identifies suicidal attempters in patients with major depressive disorder using machine learning. Psychophysiology 2022;:e14136. [PMID: 35767231 DOI: 10.1111/psyp.14136] [Reference Citation Analysis]