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For: Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK, Trivedi MH, Cannon TD, Krystal JH, Corlett PR. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry. 2016;3:243-250. [PMID: 26803397 DOI: 10.1016/s2215-0366(15)00471-x] [Cited by in Crossref: 275] [Cited by in F6Publishing: 100] [Article Influence: 55.0] [Reference Citation Analysis]
Number Citing Articles
1 van Veen SMP, Ruissen AM, Widdershoven GAM. Irremediable Psychiatric Suffering in the Context of Physician-assisted Death: A Scoping Review of Arguments: La souffrance psychiatrique irrémédiable dans le contexte du suicide assisté : Une revue étendue des arguments. Can J Psychiatry 2020;65:593-603. [PMID: 32427501 DOI: 10.1177/0706743720923072] [Cited by in Crossref: 9] [Cited by in F6Publishing: 2] [Article Influence: 9.0] [Reference Citation Analysis]
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3 Zheutlin AB, Chekroud AM, Polimanti R, Gelernter J, Sabb FW, Bilder RM, Freimer N, London ED, Hultman CM, Cannon TD. Multivariate Pattern Analysis of Genotype-Phenotype Relationships in Schizophrenia. Schizophr Bull 2018;44:1045-52. [PMID: 29534239 DOI: 10.1093/schbul/sby005] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 3.5] [Reference Citation Analysis]
4 Dipnall JF, Pasco JA, Berk M, Williams LJ, Dodd S, Jacka FN, Meyer D. Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample. PLoS One 2016;11:e0167055. [PMID: 27935995 DOI: 10.1371/journal.pone.0167055] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
5 Shaw J, Rudzicz F, Jamieson T, Goldfarb A. Artificial Intelligence and the Implementation Challenge. J Med Internet Res 2019;21:e13659. [PMID: 31293245 DOI: 10.2196/13659] [Cited by in Crossref: 53] [Cited by in F6Publishing: 24] [Article Influence: 26.5] [Reference Citation Analysis]
6 Koutsouleris N, Kambeitz-Ilankovic L, Ruhrmann S, Rosen M, Ruef A, Dwyer DB, Paolini M, Chisholm K, Kambeitz J, Haidl T, Schmidt A, Gillam J, Schultze-Lutter F, Falkai P, Reiser M, Riecher-Rössler A, Upthegrove R, Hietala J, Salokangas RKR, Pantelis C, Meisenzahl E, Wood SJ, Beque D, Brambilla P, Borgwardt S; PRONIA Consortium. Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis. JAMA Psychiatry 2018;75:1156-72. [PMID: 30267047 DOI: 10.1001/jamapsychiatry.2018.2165] [Cited by in Crossref: 131] [Cited by in F6Publishing: 87] [Article Influence: 65.5] [Reference Citation Analysis]
7 Buckman JEJ, Saunders R, Stott J, Arundell LL, O'Driscoll C, Davies MR, Eley TC, Hollon SD, Kendrick T, Ambler G, Cohen ZD, Watkins E, Gilbody S, Wiles N, Kessler D, Richards D, Brabyn S, Littlewood E, DeRubeis RJ, Lewis G, Pilling S. Role of age, gender and marital status in prognosis for adults with depression: An individual patient data meta-analysis. Epidemiol Psychiatr Sci 2021;30:e42. [PMID: 34085616 DOI: 10.1017/S2045796021000342] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Norbury A, Seymour B. Response heterogeneity: Challenges for personalised medicine and big data approaches in psychiatry and chronic pain. F1000Res 2018;7:55. [PMID: 29527298 DOI: 10.12688/f1000research.13723.2] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Sakurai H, Suzuki T, Yoshimura K, Mimura M, Uchida H. Predicting relapse with individual residual symptoms in major depressive disorder: a reanalysis of the STAR*D data. Psychopharmacology (Berl) 2017;234:2453-61. [PMID: 28470399 DOI: 10.1007/s00213-017-4634-5] [Cited by in Crossref: 23] [Cited by in F6Publishing: 17] [Article Influence: 5.8] [Reference Citation Analysis]
10 Hammond R, Athanasiadou R, Curado S, Aphinyanaphongs Y, Abrams C, Messito MJ, Gross R, Katzow M, Jay M, Razavian N, Elbel B. Predicting childhood obesity using electronic health records and publicly available data. PLoS One 2019;14:e0215571. [PMID: 31009509 DOI: 10.1371/journal.pone.0215571] [Cited by in Crossref: 19] [Cited by in F6Publishing: 6] [Article Influence: 9.5] [Reference Citation Analysis]
11 Lin E, Kuo PH, Liu YL, Yu YW, Yang AC, Tsai SJ. Prediction of Antidepressant Treatment Response and Remission Using an Ensemble Machine Learning Framework. Pharmaceuticals (Basel) 2020;13:E305. [PMID: 33065962 DOI: 10.3390/ph13100305] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 7.0] [Reference Citation Analysis]
12 Kautzky A, Möller HJ, Dold M, Bartova L, Seemüller F, Laux G, Riedel M, Gaebel W, Kasper S. Combining machine learning algorithms for prediction of antidepressant treatment response. Acta Psychiatr Scand 2021;143:36-49. [PMID: 33141944 DOI: 10.1111/acps.13250] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
13 Hu M, Shu X, Yu G, Wu X, Välimäki M, Feng H. A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study. J Med Internet Res 2021;23:e20298. [PMID: 33625369 DOI: 10.2196/20298] [Reference Citation Analysis]
14 Bodnar CN, Morganti JM, Bachstetter AD. Depression following a traumatic brain injury: uncovering cytokine dysregulation as a pathogenic mechanism. Neural Regen Res 2018;13:1693-704. [PMID: 30136679 DOI: 10.4103/1673-5374.238604] [Cited by in Crossref: 20] [Cited by in F6Publishing: 15] [Article Influence: 6.7] [Reference Citation Analysis]
15 de Nijs J, Burger TJ, Janssen RJ, Kia SM, van Opstal DPJ, de Koning MB, de Haan L, Cahn W, Schnack HG; GROUP investigators. Individualized prediction of three- and six-year outcomes of psychosis in a longitudinal multicenter study: a machine learning approach. NPJ Schizophr 2021;7:34. [PMID: 34215752 DOI: 10.1038/s41537-021-00162-3] [Reference Citation Analysis]
16 Alonso SG, de la Torre-Díez I, Hamrioui S, López-Coronado M, Barreno DC, Nozaleda LM, Franco M. Data Mining Algorithms and Techniques in Mental Health: A Systematic Review. J Med Syst 2018;42:161. [PMID: 30030644 DOI: 10.1007/s10916-018-1018-2] [Cited by in Crossref: 28] [Cited by in F6Publishing: 10] [Article Influence: 9.3] [Reference Citation Analysis]
17 Cho G, Yim J, Choi Y, Ko J, Lee SH. Review of Machine Learning Algorithms for Diagnosing Mental Illness. Psychiatry Investig 2019;16:262-9. [PMID: 30947496 DOI: 10.30773/pi.2018.12.21.2] [Cited by in Crossref: 24] [Cited by in F6Publishing: 5] [Article Influence: 12.0] [Reference Citation Analysis]
18 Richter T, Fishbain B, Markus A, Richter-Levin G, Okon-Singer H. Using machine learning-based analysis for behavioral differentiation between anxiety and depression. Sci Rep 2020;10:16381. [PMID: 33009424 DOI: 10.1038/s41598-020-72289-9] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 12.0] [Reference Citation Analysis]
19 Stern S, Linker S, Vadodaria KC, Marchetto MC, Gage FH. Prediction of response to drug therapy in psychiatric disorders. Open Biol. 2018;8. [PMID: 29794033 DOI: 10.1098/rsob.180031] [Cited by in Crossref: 28] [Cited by in F6Publishing: 14] [Article Influence: 14.0] [Reference Citation Analysis]
20 Low DM, Bentley KH, Ghosh SS. Automated assessment of psychiatric disorders using speech: A systematic review. Laryngoscope Investig Otolaryngol 2020;5:96-116. [PMID: 32128436 DOI: 10.1002/lio2.354] [Cited by in Crossref: 32] [Cited by in F6Publishing: 16] [Article Influence: 32.0] [Reference Citation Analysis]
21 Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou MM, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021;41:1427-73. [PMID: 33295676 DOI: 10.1002/med.21764] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
22 Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim HC, Jeste DV. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Curr Psychiatry Rep 2019;21:116. [PMID: 31701320 DOI: 10.1007/s11920-019-1094-0] [Cited by in Crossref: 48] [Cited by in F6Publishing: 23] [Article Influence: 24.0] [Reference Citation Analysis]
23 Buckman JEJ, Saunders R, Cohen ZD, Clarke K, Ambler G, DeRubeis RJ, Gilbody S, Hollon SD, Kendrick T, Watkins E, White IR, Lewis G, Pilling S. What factors indicate prognosis for adults with depression in primary care? A protocol for meta-analyses of individual patient data using the Dep-GP database. Wellcome Open Res 2019;4:69. [PMID: 31815189 DOI: 10.12688/wellcomeopenres.15225.3] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 6.0] [Reference Citation Analysis]
24 Shani R, Tal S, Zilcha-Mano S, Okon-Singer H. Can Machine Learning Approaches Lead Toward Personalized Cognitive Training? Front Behav Neurosci 2019;13:64. [PMID: 31019455 DOI: 10.3389/fnbeh.2019.00064] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 5.5] [Reference Citation Analysis]
25 Moise N, Wainberg M, Shah RN. Primary care and mental health: Where do we go from here? World J Psychiatry 2021;11:271-6. [PMID: 34327121 DOI: 10.5498/wjp.v11.i7.271] [Reference Citation Analysis]
26 de Vries YA, Roest AM, Bos EH, Burgerhof JGM, van Loo HM, de Jonge P. Predicting antidepressant response by monitoring early improvement of individual symptoms of depression: individual patient data meta-analysis. Br J Psychiatry 2019;214:4-10. [PMID: 29952277 DOI: 10.1192/bjp.2018.122] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 3.3] [Reference Citation Analysis]
27 Chang B, Choi Y, Jeon M, Lee J, Han KM, Kim A, Ham BJ, Kang J. ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder. Genes (Basel) 2019;10:E907. [PMID: 31703457 DOI: 10.3390/genes10110907] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
28 Perna G, Alciati A, Daccò S, Grassi M, Caldirola D. Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables. Psychiatry Investig 2020;17:193-206. [PMID: 32160691 DOI: 10.30773/pi.2019.0289] [Cited by in Crossref: 10] [Cited by in F6Publishing: 4] [Article Influence: 10.0] [Reference Citation Analysis]
29 Fusar-Poli P, Werbeloff N, Rutigliano G, Oliver D, Davies C, Stahl D, McGuire P, Osborn D. Transdiagnostic Risk Calculator for the Automatic Detection of Individuals at Risk and the Prediction of Psychosis: Second Replication in an Independent National Health Service Trust. Schizophr Bull 2019;45:562-70. [PMID: 29897527 DOI: 10.1093/schbul/sby070] [Cited by in Crossref: 43] [Cited by in F6Publishing: 40] [Article Influence: 43.0] [Reference Citation Analysis]
30 Lee EE, Torous J, De Choudhury M, Depp CA, Graham SA, Kim HC, Paulus MP, Krystal JH, Jeste DV. Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom. Biol Psychiatry Cogn Neurosci Neuroimaging 2021;6:856-64. [PMID: 33571718 DOI: 10.1016/j.bpsc.2021.02.001] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
31 Salazar de Pablo G, Studerus E, Vaquerizo-Serrano J, Irving J, Catalan A, Oliver D, Baldwin H, Danese A, Fazel S, Steyerberg EW, Stahl D, Fusar-Poli P. Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice. Schizophr Bull 2021;47:284-97. [PMID: 32914178 DOI: 10.1093/schbul/sbaa120] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 15.0] [Reference Citation Analysis]
32 Chen D, Afzal N, Sohn S, Habermann EB, Naessens JM, Larson DW, Liu H. Postoperative bleeding risk prediction for patients undergoing colorectal surgery. Surgery 2018;164:1209-16. [PMID: 30033185 DOI: 10.1016/j.surg.2018.05.043] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 2.3] [Reference Citation Analysis]
33 Moriarty AS, Meader N, Snell KI, Riley RD, Paton LW, Chew-Graham CA, Gilbody S, Churchill R, Phillips RS, Ali S, McMillan D. Prognostic models for predicting relapse or recurrence of major depressive disorder in adults. Cochrane Database Syst Rev 2021;5:CD013491. [PMID: 33956992 DOI: 10.1002/14651858.CD013491.pub2] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
34 Blitz R, Storck M, Baune BT, Dugas M, Opel N. Design and Implementation of an Informatics Infrastructure for Standardized Data Acquisition, Transfer, Storage, and Export in Psychiatric Clinical Routine: Feasibility Study. JMIR Ment Health 2021;8:e26681. [PMID: 34106072 DOI: 10.2196/26681] [Reference Citation Analysis]
35 Webb CA, Rosso IM, Rauch SL. Internet-Based Cognitive-Behavioral Therapy for Depression: Current Progress and Future Directions. Harv Rev Psychiatry 2017;25:114-22. [PMID: 28475503 DOI: 10.1097/HRP.0000000000000139] [Cited by in Crossref: 49] [Cited by in F6Publishing: 20] [Article Influence: 16.3] [Reference Citation Analysis]
36 Fineberg SK, Stahl D, Corlett P. Computational Psychiatry in Borderline Personality Disorder. Curr Behav Neurosci Rep 2017;4:31-40. [PMID: 28690972 DOI: 10.1007/s40473-017-0104-y] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
37 Kautzky A, Dold M, Bartova L, Spies M, Kranz GS, Souery D, Montgomery S, Mendlewicz J, Zohar J, Fabbri C, Serretti A, Lanzenberger R, Dikeos D, Rujescu D, Kasper S. Clinical factors predicting treatment resistant depression: affirmative results from the European multicenter study. Acta Psychiatr Scand 2019;139:78-88. [PMID: 30291625 DOI: 10.1111/acps.12959] [Cited by in Crossref: 38] [Cited by in F6Publishing: 23] [Article Influence: 12.7] [Reference Citation Analysis]
38 Niehoff KM, Mecca MC, Fried TR. Medication appropriateness criteria for older adults: a narrative review of criteria and supporting studies. Ther Adv Drug Saf 2019;10:2042098618815431. [PMID: 30719279 DOI: 10.1177/2042098618815431] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
39 Finnegan SL, Pattinson KTS, Sundh J, Sköld M, Janson C, Blomberg A, Sandberg J, Ekström M. A common model for the breathlessness experience across cardiorespiratory disease. ERJ Open Res 2021;7:00818-2020. [PMID: 34195256 DOI: 10.1183/23120541.00818-2020] [Reference Citation Analysis]
40 Čukić M, López V, Pavón J. Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review. J Med Internet Res 2020;22:e19548. [PMID: 33141088 DOI: 10.2196/19548] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
41 Maj M. Beyond diagnosis in psychiatric practice. Ann Gen Psychiatry 2020;19:27. [PMID: 32322290 DOI: 10.1186/s12991-020-00279-2] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 8.0] [Reference Citation Analysis]
42 Jollans L, Whelan R. Neuromarkers for Mental Disorders: Harnessing Population Neuroscience. Front Psychiatry 2018;9:242. [PMID: 29928237 DOI: 10.3389/fpsyt.2018.00242] [Cited by in Crossref: 26] [Cited by in F6Publishing: 14] [Article Influence: 8.7] [Reference Citation Analysis]
43 Pearson R, Pisner D, Meyer B, Shumake J, Beevers CG. A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression. Psychol Med 2019;49:2330-41. [PMID: 30392475 DOI: 10.1017/S003329171800315X] [Cited by in Crossref: 10] [Cited by in F6Publishing: 4] [Article Influence: 3.3] [Reference Citation Analysis]
44 Buckman JEJ, Saunders R, Cohen ZD, Barnett P, Clarke K, Ambler G, DeRubeis RJ, Gilbody S, Hollon SD, Kendrick T, Watkins E, Wiles N, Kessler D, Richards D, Sharp D, Brabyn S, Littlewood E, Salisbury C, White IR, Lewis G, Pilling S. The contribution of depressive 'disorder characteristics' to determinations of prognosis for adults with depression: an individual patient data meta-analysis. Psychol Med 2021;51:1068-81. [PMID: 33849685 DOI: 10.1017/S0033291721001367] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
45 Tran BX, McIntyre RS, Latkin CA, Phan HT, Vu GT, Nguyen HLT, Gwee KK, Ho CSH, Ho RCM. The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis. Int J Environ Res Public Health 2019;16:E2150. [PMID: 31216619 DOI: 10.3390/ijerph16122150] [Cited by in Crossref: 29] [Cited by in F6Publishing: 12] [Article Influence: 14.5] [Reference Citation Analysis]
46 Jacobs M, Pradier MF, McCoy TH Jr, Perlis RH, Doshi-Velez F, Gajos KZ. How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection. Transl Psychiatry 2021;11:108. [PMID: 33542191 DOI: 10.1038/s41398-021-01224-x] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
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48 Gauthier G, Guérin A, Zhdanava M, Jacobson W, Nomikos G, Merikle E, François C, Perez V. Treatment patterns, healthcare resource utilization, and costs following first-line antidepressant treatment in major depressive disorder: a retrospective US claims database analysis. BMC Psychiatry 2017;17:222. [PMID: 28629442 DOI: 10.1186/s12888-017-1385-0] [Cited by in Crossref: 18] [Cited by in F6Publishing: 13] [Article Influence: 4.5] [Reference Citation Analysis]
49 Amare AT, Schubert KO, Tekola-Ayele F, Hsu YH, Sangkuhl K, Jenkins G, Whaley RM, Barman P, Batzler A, Altman RB, Arolt V, Brockmöller J, Chen CH, Domschke K, Hall-Flavin DK, Hong CJ, Illi A, Ji Y, Kampman O, Kinoshita T, Leinonen E, Liou YJ, Mushiroda T, Nonen S, Skime MK, Wang L, Kato M, Liu YL, Praphanphoj V, Stingl JC, Bobo WV, Tsai SJ, Kubo M, Klein TE, Weinshilboum RM, Biernacka JM, Baune BT. The association of obesity and coronary artery disease genes with response to SSRIs treatment in major depression. J Neural Transm (Vienna) 2019;126:35-45. [PMID: 30610379 DOI: 10.1007/s00702-018-01966-x] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 5.0] [Reference Citation Analysis]
50 Borrione L, Bellini H, Razza LB, Avila AG, Baeken C, Brem AK, Busatto G, Carvalho AF, Chekroud A, Daskalakis ZJ, Deng ZD, Downar J, Gattaz W, Loo C, Lotufo PA, Martin MDGM, McClintock SM, O'Shea J, Padberg F, Passos IC, Salum GA, Vanderhasselt MA, Fraguas R, Benseñor I, Valiengo L, Brunoni AR. Precision non-implantable neuromodulation therapies: a perspective for the depressed brain. Braz J Psychiatry 2020;42:403-19. [PMID: 32187319 DOI: 10.1590/1516-4446-2019-0741] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 9.0] [Reference Citation Analysis]
51 Bar-Yosef T, Hussein W, Yitzhaki O, Damri O, Givon L, Marom C, Gurman V, Levine J, Bersudsky Y, Agam G, Ben-Shachar D. Mitochondrial function parameters as a tool for tailored drug treatment of an individual with psychosis: a proof of concept study. Sci Rep 2020;10:12258. [PMID: 32703977 DOI: 10.1038/s41598-020-69207-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
52 Nunez JJ, Nguyen TT, Zhou Y, Cao B, Ng RT, Chen J, Frey BN, Milev R, Müller DJ, Rotzinger S, Soares CN, Uher R, Kennedy SH, Lam RW. Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1. PLoS One 2021;16:e0253023. [PMID: 34181661 DOI: 10.1371/journal.pone.0253023] [Reference Citation Analysis]
53 Amare AT, Schubert KO, Baune BT. Pharmacogenomics in the treatment of mood disorders: Strategies and Opportunities for personalized psychiatry. EPMA J 2017;8:211-27. [PMID: 29021832 DOI: 10.1007/s13167-017-0112-8] [Cited by in Crossref: 46] [Cited by in F6Publishing: 30] [Article Influence: 11.5] [Reference Citation Analysis]
54 Malda A, Boonstra N, Barf H, de Jong S, Aleman A, Addington J, Pruessner M, Nieman D, de Haan L, Morrison A, Riecher-Rössler A, Studerus E, Ruhrmann S, Schultze-Lutter F, An SK, Koike S, Kasai K, Nelson B, McGorry P, Wood S, Lin A, Yung AY, Kotlicka-Antczak M, Armando M, Vicari S, Katsura M, Matsumoto K, Durston S, Ziermans T, Wunderink L, Ising H, van der Gaag M, Fusar-Poli P, Pijnenborg GHM. Individualized Prediction of Transition to Psychosis in 1,676 Individuals at Clinical High Risk: Development and Validation of a Multivariable Prediction Model Based on Individual Patient Data Meta-Analysis. Front Psychiatry 2019;10:345. [PMID: 31178767 DOI: 10.3389/fpsyt.2019.00345] [Cited by in Crossref: 14] [Cited by in F6Publishing: 9] [Article Influence: 7.0] [Reference Citation Analysis]
55 Jollans L, Boyle R, Artiges E, Banaschewski T, Desrivières S, Grigis A, Martinot JL, Paus T, Smolka MN, Walter H, Schumann G, Garavan H, Whelan R. Quantifying performance of machine learning methods for neuroimaging data. Neuroimage 2019;199:351-65. [PMID: 31173905 DOI: 10.1016/j.neuroimage.2019.05.082] [Cited by in Crossref: 50] [Cited by in F6Publishing: 21] [Article Influence: 25.0] [Reference Citation Analysis]
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57 Hughes MC, Pradier MF, Ross AS, McCoy TH Jr, Perlis RH, Doshi-Velez F. Assessment of a Prediction Model for Antidepressant Treatment Stability Using Supervised Topic Models. JAMA Netw Open 2020;3:e205308. [PMID: 32432711 DOI: 10.1001/jamanetworkopen.2020.5308] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
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