BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Claude LA, Houenou J, Duchesnay E, Favre P. Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions. Bipolar Disord 2020;22:334-55. [PMID: 32108409 DOI: 10.1111/bdi.12895] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Liu YS, Chokka S, Cao B, Chokka PR. Screening for bipolar disorder in a tertiary mental health centre using EarlyDetect: A machine learning-based pilot study. Journal of Affective Disorders Reports 2021;6:100215. [DOI: 10.1016/j.jadr.2021.100215] [Reference Citation Analysis]
2 Yang T, Frangou S, Lam RW, Huang J, Su Y, Zhao G, Mao R, Zhu N, Zhou R, Lin X, Xia W, Wang X, Wang Y, Peng D, Wang Z, Yatham LN, Chen J, Fang Y. Probing the clinical and brain structural boundaries of bipolar and major depressive disorder. Transl Psychiatry 2021;11:48. [PMID: 33446647 DOI: 10.1038/s41398-020-01169-7] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
3 Sánchez-Morla EM, Fuentes JL, Miguel-Jiménez JM, Boquete L, Ortiz M, Orduna E, Satue M, Garcia-Martin E. Automatic Diagnosis of Bipolar Disorder Using Optical Coherence Tomography Data and Artificial Intelligence. J Pers Med 2021;11:803. [PMID: 34442447 DOI: 10.3390/jpm11080803] [Reference Citation Analysis]
4 Chen YL, Tu PC, Huang TH, Bai YM, Su TP, Chen MH, Wu YT. Using Minimal-Redundant and Maximal-Relevant Whole-Brain Functional Connectivity to Classify Bipolar Disorder. Front Neurosci 2020;14:563368. [PMID: 33192250 DOI: 10.3389/fnins.2020.563368] [Reference Citation Analysis]
5 Zhao M, Feng Z. Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study. Neuropsychiatr Dis Treat 2020;16:2743-52. [PMID: 33209029 DOI: 10.2147/NDT.S275620] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Zhu R, Tian S, Wang H, Jiang H, Wang X, Shao J, Wang Q, Yan R, Tao S, Liu H, Yao Z, Lu Q. Discriminating Suicide Attempters and Predicting Suicide Risk Using Altered Frontolimbic Resting-State Functional Connectivity in Patients With Bipolar II Disorder. Front Psychiatry 2020;11:597770. [PMID: 33324262 DOI: 10.3389/fpsyt.2020.597770] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
7 Colombo F, Calesella F, Mazza MG, Melloni EMT, Morelli MJ, Scotti GM, Benedetti F, Bollettini I, Vai B. Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: a systematic review and meta-analysis. Neurosci Biobehav Rev 2022;:104552. [PMID: 35120970 DOI: 10.1016/j.neubiorev.2022.104552] [Reference Citation Analysis]
8 Sonkurt HO, Altınöz AE, Çimen E, Köşger F, Öztürk G. The role of cognitive functions in the diagnosis of bipolar disorder: A machine learning model. Int J Med Inform 2021;145:104311. [PMID: 33202371 DOI: 10.1016/j.ijmedinf.2020.104311] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
9 Lei D, Qin K, Li W, Pinaya WHL, Tallman MJ, Patino LR, Strawn JR, Fleck D, Klein CC, Lui S, Gong Q, Adler CM, Mechelli A, Sweeney JA, DelBello MP. Brain morphometric features predict medication response in youth with bipolar disorder: a prospective randomized clinical trial. Psychol Med 2022;:1-11. [PMID: 35392995 DOI: 10.1017/S0033291722000757] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]