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For: Chekroud AM, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, Cohen Z, Belgrave D, DeRubeis R, Iniesta R, Dwyer D, Choi K. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 2021;20:154-70. [PMID: 34002503 DOI: 10.1002/wps.20882] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Rema J, Novais F, Telles-Correia D. Precision Psychiatry: Machine learning as a tool to find new pharmacological targets. Curr Top Med Chem 2021. [PMID: 34607546 DOI: 10.2174/1568026621666211004095917] [Reference Citation Analysis]
2 Meehan AJ, Lewis SJ, Fazel S, Fusar-Poli P, Steyerberg EW, Stahl D, Danese A. Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges. Mol Psychiatry 2022. [PMID: 35365801 DOI: 10.1038/s41380-022-01528-4] [Reference Citation Analysis]
3 Brunet A, Rivest-Beauregard M, Lonergan M, Cipolletta S, Rasmussen A, Meng X, Jaafari N, Romero S, Superka J, Brown AD, Sapkota RP. PTSD is not the emblematic disorder of the COVID-19 pandemic; adjustment disorder is. BMC Psychiatry 2022;22:300. [PMID: 35484539 DOI: 10.1186/s12888-022-03903-5] [Reference Citation Analysis]
4 Hsu CW, Tsai SY, Wang LJ, Liang CS, Carvalho AF, Solmi M, Vieta E, Lin PY, Hu CA, Kao HY. Predicting Serum Levels of Lithium-Treated Patients: A Supervised Machine Learning Approach. Biomedicines 2021;9:1558. [PMID: 34829787 DOI: 10.3390/biomedicines9111558] [Reference Citation Analysis]
5 Silva B, Gholam M, Golay P, Bonsack C, Morandi S. Predicting involuntary hospitalization in psychiatry: A machine learning investigation. Eur Psychiatry 2021;64:e48. [PMID: 34233774 DOI: 10.1192/j.eurpsy.2021.2220] [Reference Citation Analysis]
6 Dwyer D, Koutsouleris N. Annual Research Review: Translational machine learning for child and adolescent psychiatry. J Child Psychol Psychiatry 2022. [PMID: 35040130 DOI: 10.1111/jcpp.13545] [Reference Citation Analysis]
7 Gao C, Xu Z, Tan T, Chen Z, Shen T, Chen L, Tan H, Chen B, Zhang Z, Yuan Y. Combination of Spontaneous Regional Brain Activity and HTR1A/1B DNA Methylation to Predict Early Responses to Antidepressant Treatments in MDD. Journal of Affective Disorders 2022. [DOI: 10.1016/j.jad.2022.01.098] [Reference Citation Analysis]
8 Liu D, Wang X, Li L, Jiang Q, Li X, Liu M, Wang W, Shi E, Zhang C, Wang Y, Zhang Y, Wang L. Machine Learning-Based Model for the Prognosis of Postoperative Gastric Cancer. CMAR 2022;Volume 14:135-55. [DOI: 10.2147/cmar.s342352] [Reference Citation Analysis]
9 Forrest LN, Ivezaj V, Grilo CM. Machine learning v. traditional regression models predicting treatment outcomes for binge-eating disorder from a randomized controlled trial. Psychol Med 2021;:1-12. [PMID: 34819195 DOI: 10.1017/S0033291721004748] [Reference Citation Analysis]
10 Lutz W, Rubel J, Deisenhofer AK, Moggia D. Continuous outcome measurement in modern data-informed psychotherapies. World Psychiatry 2022;21:215-6. [PMID: 35524594 DOI: 10.1002/wps.20988] [Reference Citation Analysis]
11 Miranda L, Paul R, Pütz B, Koutsouleris N, Müller-Myhsok B. Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping. Front Psychiatry 2021;12:665536. [PMID: 34744805 DOI: 10.3389/fpsyt.2021.665536] [Reference Citation Analysis]
12 Mcnamara ME, Zisser M, Beevers CG, Shumake J. Not just “big” data: Importance of sample size, measurement error, and uninformative predictors for developing prognostic models for digital interventions. Behaviour Research and Therapy 2022. [DOI: 10.1016/j.brat.2022.104086] [Reference Citation Analysis]
13 Cohen ZD, Derubeis RJ, Hayes R, Watkins ER, Lewis G, Byng R, Byford S, Crane C, Kuyken W, Dalgleish T, Schweizer S. The Development and Internal Evaluation of a Predictive Model to Identify for Whom Mindfulness-Based Cognitive Therapy Offers Superior Relapse Prevention for Recurrent Depression Versus Maintenance Antidepressant Medication. Clinical Psychological Science. [DOI: 10.1177/21677026221076832] [Reference Citation Analysis]
14 Renn BN, Schurr M, Zaslavsky O, Pratap A. Artificial Intelligence: An Interprofessional Perspective on Implications for Geriatric Mental Health Research and Care. Front Psychiatry 2021;12:734909. [PMID: 34867524 DOI: 10.3389/fpsyt.2021.734909] [Reference Citation Analysis]
15 Balcombe L, De Leo D. Digital Mental Health Amid COVID-19. Encyclopedia 2021;1:1047-57. [DOI: 10.3390/encyclopedia1040080] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Kessler RC, Kazdin AE, Aguilar-Gaxiola S, Al-Hamzawi A, Alonso J, Altwaijri YA, Andrade LH, Benjet C, Bharat C, Borges G, Bruffaerts R, Bunting B, de Almeida JMC, Cardoso G, Chiu WT, Cía A, Ciutan M, Degenhardt L, de Girolamo G, de Jonge P, de Vries YA, Florescu S, Gureje O, Haro JM, Harris MG, Hu C, Karam AN, Karam EG, Karam G, Kawakami N, Kiejna A, Kovess-Masfety V, Lee S, Makanjuola V, McGrath JJ, Medina-Mora ME, Moskalewicz J, Navarro-Mateu F, Nierenberg AA, Nishi D, Ojagbemi A, Oladeji BD, O'Neill S, Posada-Villa J, Puac-Polanco V, Rapsey C, Ruscio AM, Sampson NA, Scott KM, Slade T, Stagnaro JC, Stein DJ, Tachimori H, Ten Have M, Torres Y, Viana MC, Vigo DV, Williams DR, Wojtyniak B, Xavier M, Zarkov Z, Ziobrowski HN; WHO World Mental Health Survey collaborators. Patterns and correlates of patient-reported helpfulness of treatment for common mental and substance use disorders in the WHO World Mental Health Surveys. World Psychiatry 2022;21:272-86. [PMID: 35524618 DOI: 10.1002/wps.20971] [Reference Citation Analysis]
17 Roe D, Slade M, Jones N. The utility of patient-reported outcome measures in mental health. World Psychiatry 2022;21:56-7. [PMID: 35015343 DOI: 10.1002/wps.20924] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Kim S, Lee K. Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods. Neuropsychiatr Dis Treat 2021;17:3415-30. [PMID: 34848962 DOI: 10.2147/NDT.S339412] [Reference Citation Analysis]
19 Kim S, Lee HK, Lee K. Screening of Mood Symptoms Using MMPI-2-RF Scales: An Application of Machine Learning Techniques. J Pers Med 2021;11:812. [PMID: 34442456 DOI: 10.3390/jpm11080812] [Reference Citation Analysis]
20 Zhou Y, Chen X, Liu D, Pan Y, Hou Y, Gao T, Peng F, Wang X, Zhang X. Predicting first session working alliances using deep learning algorithms: A proof-of-concept study for personalized psychotherapy. Psychotherapy Research. [DOI: 10.1080/10503307.2022.2078680] [Reference Citation Analysis]
21 Alemi F, Min H, Yousefi M, Becker LK, Hane CA, Nori VS, Wojtusiak J. Effectiveness of common antidepressants: a post market release study. EClinicalMedicine 2021;41:101171. [PMID: 34877511 DOI: 10.1016/j.eclinm.2021.101171] [Reference Citation Analysis]
22 Held P, Schubert RA, Pridgen S, Kovacevic M, Montes M, Christ NM, Banerjee U, Smith DL. Who will respond to intensive PTSD treatment? A machine learning approach to predicting response prior to starting treatment. Journal of Psychiatric Research 2022;151:78-85. [DOI: 10.1016/j.jpsychires.2022.03.066] [Reference Citation Analysis]
23 Paton LW, Tiffin PA. Technology Matters: Machine learning approaches to personalised child and adolescent mental health care. Child Adolesc Ment Health 2022. [PMID: 35218142 DOI: 10.1111/camh.12546] [Reference Citation Analysis]
24 Dwyer D, Krishnadas R. Five points to consider when reading a translational machine-learning paper. Br J Psychiatry 2022;220:169-71. [PMID: 35354505 DOI: 10.1192/bjp.2022.29] [Reference Citation Analysis]
25 Bae YJ, Shim M, Lee WH. Schizophrenia Detection Using Machine Learning Approach from Social Media Content. Sensors (Basel) 2021;21:5924. [PMID: 34502815 DOI: 10.3390/s21175924] [Reference Citation Analysis]
26 Fardouly J, Crosby RD, Sukunesan S. Potential benefits and limitations of machine learning in the field of eating disorders: current research and future directions. J Eat Disord 2022;10. [DOI: 10.1186/s40337-022-00581-2] [Reference Citation Analysis]
27 Linardon J, Fuller-Tyszkiewicz M, Shatte A, Greenwood CJ. An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms. Int J Eat Disord 2022. [PMID: 35560256 DOI: 10.1002/eat.23733] [Reference Citation Analysis]
28 Rost N, Binder EB, Brückl TM. Predicting treatment outcome in depression: an introduction into current concepts and challenges. Eur Arch Psychiatry Clin Neurosci 2022. [PMID: 35587279 DOI: 10.1007/s00406-022-01418-4] [Reference Citation Analysis]
29 Lutz W. Data-Informed Advances and Technology Augmentation. Cognitive and Behavioral Practice 2022. [DOI: 10.1016/j.cbpra.2022.02.008] [Reference Citation Analysis]