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For: Hilbert K, Lueken U, Muehlhan M, Beesdo-Baum K. Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study. Brain Behav 2017;7:e00633. [PMID: 28293473 DOI: 10.1002/brb3.633] [Cited by in Crossref: 42] [Cited by in F6Publishing: 29] [Article Influence: 8.4] [Reference Citation Analysis]
Number Citing Articles
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2 Walter M, Alizadeh S, Jamalabadi H, Lueken U, Dannlowski U, Walter H, Olbrich S, Colic L, Kambeitz J, Koutsouleris N, Hahn T, Dwyer DB. Translational machine learning for psychiatric neuroimaging. Progress in Neuro-Psychopharmacology and Biological Psychiatry 2019;91:113-21. [DOI: 10.1016/j.pnpbp.2018.09.014] [Cited by in Crossref: 26] [Cited by in F6Publishing: 19] [Article Influence: 8.7] [Reference Citation Analysis]
3 Sharma V, Prakash NR, Kalra P. Depression status identification using autoencoder neural network. Biomedical Signal Processing and Control 2022;75:103568. [DOI: 10.1016/j.bspc.2022.103568] [Reference Citation Analysis]
4 Wen Z, Marin MF, Blackford JU, Chen ZS, Milad MR. Fear-induced brain activations distinguish anxious and trauma-exposed brains. Transl Psychiatry 2021;11:46. [PMID: 33441547 DOI: 10.1038/s41398-020-01193-7] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
5 Na KS, Kim YK. The Application of a Machine Learning-Based Brain Magnetic Resonance Imaging Approach in Major Depression. Adv Exp Med Biol 2021;1305:57-69. [PMID: 33834394 DOI: 10.1007/978-981-33-6044-0_4] [Reference Citation Analysis]
6 Tai AMY, Albuquerque A, Carmona NE, Subramanieapillai M, Cha DS, Sheko M, Lee Y, Mansur R, McIntyre RS. Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry. Artif Intell Med 2019;99:101704. [PMID: 31606109 DOI: 10.1016/j.artmed.2019.101704] [Cited by in Crossref: 29] [Cited by in F6Publishing: 12] [Article Influence: 9.7] [Reference Citation Analysis]
7 Al-ezzi A, Al-shargabi AA, Al-shargie F, Zahary AT. Complexity Analysis of EEG in Patients With Social Anxiety Disorder Using Fuzzy Entropy and Machine Learning Techniques. IEEE Access 2022;10:39926-38. [DOI: 10.1109/access.2022.3165199] [Reference Citation Analysis]
8 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]
9 Lytras MD, Chui KT, Visvizi A. Data Analytics in Smart Healthcare: The Recent Developments and Beyond. Applied Sciences 2019;9:2812. [DOI: 10.3390/app9142812] [Cited by in Crossref: 16] [Cited by in F6Publishing: 1] [Article Influence: 5.3] [Reference Citation Analysis]
10 Bokma WA, Zhutovsky P, Giltay EJ, Schoevers RA, Penninx BW, van Balkom AL, Batelaan NM, van Wingen GA. Predicting the naturalistic course in anxiety disorders using clinical and biological markers: a machine learning approach. Psychol Med . [DOI: 10.1017/s0033291720001658] [Cited by in Crossref: 4] [Article Influence: 2.0] [Reference Citation Analysis]
11 Aleem S, Huda NU, Amin R, Khalid S, Alshamrani SS, Alshehri A. Machine Learning Algorithms for Depression: Diagnosis, Insights, and Research Directions. Electronics 2022;11:1111. [DOI: 10.3390/electronics11071111] [Reference Citation Analysis]
12 Aydin O, Unal Aydin P, Arslan A. Development of Neuroimaging-Based Biomarkers in Psychiatry. Adv Exp Med Biol 2019;1192:159-95. [PMID: 31705495 DOI: 10.1007/978-981-32-9721-0_9] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
13 Zhang Z, Liao M, Yao Z, Hu B, Xie Y, Zheng W, Hu T, Zhao Y, Yang F, Zhang Y, Su L, Li L, Gutknecht J, Majoe D. Frequency-Specific Functional Connectivity Density as an Effective Biomarker for Adolescent Generalized Anxiety Disorder. Front Hum Neurosci 2017;11:549. [PMID: 29259549 DOI: 10.3389/fnhum.2017.00549] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 2.2] [Reference Citation Analysis]
14 Xiong H, Berkovsky S, Romano M, Sharan RV, Liu S, Coiera E, McLellan LF. Prediction of anxiety disorders using a feature ensemble based bayesian neural network. J Biomed Inform 2021;123:103921. [PMID: 34628061 DOI: 10.1016/j.jbi.2021.103921] [Reference Citation Analysis]
15 Liu K, Droncheff B, Warren SL. Predictive Utility of Symptom Measures in Classifying Anxiety and Depression: A Machine-Learning Approach. Psychiatry Research 2022. [DOI: 10.1016/j.psychres.2022.114534] [Reference Citation Analysis]
16 Chung J, Teo J, Minutolo A. Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges. Applied Computational Intelligence and Soft Computing 2022;2022:1-19. [DOI: 10.1155/2022/9970363] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
17 Hilbert K, Lueken U, Muehlhan M, Beesdo-Baum K. Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study. Brain Behav 2017;7:e00633. [PMID: 28293473 DOI: 10.1002/brb3.633] [Cited by in Crossref: 42] [Cited by in F6Publishing: 29] [Article Influence: 8.4] [Reference Citation Analysis]
18 Mühle C, Wagner CJ, Färber K, Richter-Schmidinger T, Gulbins E, Lenz B, Kornhuber J. Secretory Acid Sphingomyelinase in the Serum of Medicated Patients Predicts the Prospective Course of Depression. J Clin Med 2019;8:E846. [PMID: 31200571 DOI: 10.3390/jcm8060846] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 3.7] [Reference Citation Analysis]
19 Sun B, Zhang Y, He J, Xiao Y, Xiao R. An automatic diagnostic network using skew-robust adversarial discriminative domain adaptation to evaluate the severity of depression. Comput Methods Programs Biomed 2019;173:185-95. [PMID: 30683543 DOI: 10.1016/j.cmpb.2019.01.006] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
20 Warnell KR, Pecukonis M, Redcay E. Developmental relations between amygdala volume and anxiety traits: Effects of informant, sex, and age. Dev Psychopathol 2018;30:1503-15. [DOI: 10.1017/s0954579417001626] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
21 Shane MS, Denomme WJ. Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer. Personal Neurosci 2021;4:e6. [PMID: 34909565 DOI: 10.1017/pen.2021.2] [Reference Citation Analysis]
22 Serra-Blasco M, Radua J, Soriano-Mas C, Gómez-Benlloch A, Porta-Casteràs D, Carulla-Roig M, Albajes-Eizagirre A, Arnone D, Klauser P, Canales-Rodríguez EJ, Hilbert K, Wise T, Cheng Y, Kandilarova S, Mataix-Cols D, Vieta E, Via E, Cardoner N. Structural brain correlates in major depression, anxiety disorders and post-traumatic stress disorder: A voxel-based morphometry meta-analysis. Neurosci Biobehav Rev 2021;129:269-81. [PMID: 34256069 DOI: 10.1016/j.neubiorev.2021.07.002] [Reference Citation Analysis]
23 Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp. 2020;41:3468-3535. [PMID: 32374075 DOI: 10.1002/hbm.25013] [Cited by in Crossref: 15] [Cited by in F6Publishing: 18] [Article Influence: 7.5] [Reference Citation Analysis]
24 Maggioni E, Delvecchio G, Grottaroli M, Garzitto M, Piccin S, Bonivento C, Maieron M, D'Agostini S, Perna G, Balestrieri M, Brambilla P. Common and different neural markers in major depression and anxiety disorders: A pilot structural magnetic resonance imaging study. Psychiatry Res Neuroimaging 2019;290:42-50. [PMID: 31279954 DOI: 10.1016/j.pscychresns.2019.06.006] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 4.0] [Reference Citation Analysis]
25 Mufford MS, van der Meer D, Andreassen OA, Ramesar R, Stein DJ, Dalvie S. A review of systems biology research of anxiety disorders. Braz J Psychiatry 2021;43:414-23. [PMID: 33053074 DOI: 10.1590/1516-4446-2020-1090] [Reference Citation Analysis]
26 [DOI: 10.1109/icdabi53623.2021.9655966] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
27 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]
28 Tognin S, van Hell HH, Merritt K, Winter-van Rossum I, Bossong MG, Kempton MJ, Modinos G, Fusar-Poli P, Mechelli A, Dazzan P, Maat A, de Haan L, Crespo-Facorro B, Glenthøj B, Lawrie SM, McDonald C, Gruber O, van Amelsvoort T, Arango C, Kircher T, Nelson B, Galderisi S, Bressan R, Kwon JS, Weiser M, Mizrahi R, Sachs G, Maatz A, Kahn R, McGuire P; PSYSCAN Consortium. Towards Precision Medicine in Psychosis: Benefits and Challenges of Multimodal Multicenter Studies-PSYSCAN: Translating Neuroimaging Findings From Research into Clinical Practice. Schizophr Bull 2020;46:432-41. [PMID: 31424555 DOI: 10.1093/schbul/sbz067] [Cited by in Crossref: 29] [Cited by in F6Publishing: 24] [Article Influence: 29.0] [Reference Citation Analysis]
29 Gao S, Calhoun VD, Sui J. Machine learning in major depression: From classification to treatment outcome prediction. CNS Neurosci Ther 2018;24:1037-52. [PMID: 30136381 DOI: 10.1111/cns.13048] [Cited by in Crossref: 68] [Cited by in F6Publishing: 55] [Article Influence: 17.0] [Reference Citation Analysis]
30 Grzenda A, Speier W, Siddarth P, Pant A, Krause-Sorio B, Narr K, Lavretsky H. Machine Learning Prediction of Treatment Outcome in Late-Life Depression. Front Psychiatry 2021;12:738494. [PMID: 34744829 DOI: 10.3389/fpsyt.2021.738494] [Reference Citation Analysis]
31 Cousins A, Nakano L, Schofield E, Kabaila R. A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan. Neural Comput Appl 2022;:1-20. [PMID: 35039718 DOI: 10.1007/s00521-021-06710-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]