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For: 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: 39] [Article Influence: 9.7] [Reference Citation Analysis]
Number Citing Articles
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6 Xie Y, Ding H, Du X, Chai C, Wei X, Sun J, Zhuo C, Wang L, Li J, Tian H, Liang M, Zhang S, Yu C, Qin W. Morphometric Integrated Classification Index: A Multisite Model-Based, Interpretable, Shareable and Evolvable Biomarker for Schizophrenia. Schizophr Bull 2022:sbac096. [PMID: 35925032 DOI: 10.1093/schbul/sbac096] [Reference Citation Analysis]
7 Huang M, Zhang X, Chen X, Mai Y, Wu X, Zhao J, Feng Q. Joint-Channel-Connectivity-Based Feature Selection and Classification on fNIRS for Stress Detection in Decision-Making. IEEE Trans Neural Syst Rehabil Eng 2022;30:1858-69. [PMID: 35788456 DOI: 10.1109/TNSRE.2022.3188560] [Reference Citation Analysis]
8 Wang S, Li M, Ng SB. Research on Infant Health Diagnosis and Intelligence Development Based on Machine Learning and Health Information Statistics. Front Public Health 2022;10:846598. [PMID: 35719653 DOI: 10.3389/fpubh.2022.846598] [Reference Citation Analysis]
9 Meng X, Wang M, O’donnell KJ, Caron J, Meaney MJ, Li Y. Integrative PheWAS analysis in risk categorization of major depressive disorder and identifying their associations with genetic variants using a latent topic model approach. Transl Psychiatry 2022;12. [DOI: 10.1038/s41398-022-02015-8] [Reference Citation Analysis]
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12 Mohsin M, Ali SA, Shamim SK, Ahmad A. A GIS-based novel approach for suitable sanitary landfill site selection using integrated fuzzy analytic hierarchy process and machine learning algorithms. Environ Sci Pollut Res Int 2022;29:31511-40. [PMID: 35001277 DOI: 10.1007/s11356-021-17961-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Zhou Z, Luo D, Yang BX, Liu Z. Machine Learning-Based Prediction Models for Depression Symptoms Among Chinese Healthcare Workers During the Early COVID-19 Outbreak in 2020: A Cross-Sectional Study. Front Psychiatry 2022;13:876995. [DOI: 10.3389/fpsyt.2022.876995] [Reference Citation Analysis]
14 Rubeis G. iHealth: The ethics of artificial intelligence and big data in mental healthcare. Internet Interventions 2022;28:100518. [DOI: 10.1016/j.invent.2022.100518] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Góngora Alonso S, Marques G, Agarwal D, De la Torre Díez I, Franco-Martín M. Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia. Sensors (Basel) 2022;22:2517. [PMID: 35408133 DOI: 10.3390/s22072517] [Reference Citation Analysis]
16 Kim YK. Recent advances and challenges in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2021;111:110403. [PMID: 34293412 DOI: 10.1016/j.pnpbp.2021.110403] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
17 Kappes JR, Huber DA, Kirchebner J, Sonnweber M, Günther MP, Lau S. Self-Harm Among Forensic Psychiatric Inpatients With Schizophrenia Spectrum Disorders: An Explorative Analysis. Int J Offender Ther Comp Criminol 2021;:306624X211062139. [PMID: 34861802 DOI: 10.1177/0306624X211062139] [Reference Citation Analysis]
18 Kessler RC, Luedtke A. Pragmatic Precision Psychiatry-A New Direction for Optimizing Treatment Selection. JAMA Psychiatry 2021;78:1384-90. [PMID: 34550327 DOI: 10.1001/jamapsychiatry.2021.2500] [Cited by in Crossref: 1] [Cited by in F6Publishing: 9] [Article Influence: 1.0] [Reference Citation Analysis]
19 Grendas LN, Chiapella L, Rodante DE, Daray FM. Comparison of traditional model-based statistical methods with machine learning for the prediction of suicide behaviour. J Psychiatr Res 2021;145:85-91. [PMID: 34883411 DOI: 10.1016/j.jpsychires.2021.11.029] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Rahman MM, Paul KC, Hossain MA, Ali GGMN, Rahman MS, Thill JC. Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review. IEEE Access 2021;9:72420-50. [PMID: 34786314 DOI: 10.1109/ACCESS.2021.3079121] [Cited by in Crossref: 4] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
21 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] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Teng H. Construction and Drug Evaluation Based on Convolutional Neural Network System Optimized by Grey Correlation Analysis. Comput Intell Neurosci 2021;2021:2794588. [PMID: 34567098 DOI: 10.1155/2021/2794588] [Reference Citation Analysis]
23 Stein DJ. Evidence-Based Pharmacotherapy of Generalised Anxiety Disorder: Focus on Agomelatine. Adv Ther 2021;38:52-60. [PMID: 34417992 DOI: 10.1007/s12325-021-01860-1] [Reference Citation Analysis]
24 Gooding P, Kariotis T. Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review. JMIR Ment Health 2021;8:e24668. [PMID: 34110297 DOI: 10.2196/24668] [Cited by in Crossref: 1] [Cited by in F6Publishing: 6] [Article Influence: 1.0] [Reference Citation Analysis]
25 Andersson S, Bathula DR, Iliadis SI, Walter M, Skalkidou A. Predicting women with depressive symptoms postpartum with machine learning methods. Sci Rep 2021;11:7877. [PMID: 33846362 DOI: 10.1038/s41598-021-86368-y] [Cited by in F6Publishing: 11] [Reference Citation Analysis]
26 Komatsu H, Watanabe E, Fukuchi M. Psychiatric Neural Networks and Precision Therapeutics by Machine Learning. Biomedicines 2021;9:403. [PMID: 33917863 DOI: 10.3390/biomedicines9040403] [Cited by in Crossref: 2] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
27 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: 7] [Article Influence: 3.0] [Reference Citation Analysis]
28 Thenral M, Annamalai A. Challenges of Building, Deploying, and Using AI-Enabled Telepsychiatry Platforms for Clinical Practice Among Urban Indians: A Qualitative Study. Indian J Psychol Med 2021;43:336-42. [PMID: 34385728 DOI: 10.1177/0253717620973414] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
29 Zhu F, Pan Z, Tang Y, Fu P, Cheng S, Hou W, Zhang Q, Huang H, Sun Y. Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER. CNS Neurosci Ther 2021;27:92-100. [PMID: 33249760 DOI: 10.1111/cns.13509] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
30 Rahul K, Banyal RK. Firefly algorithm: an optimization solution in big data processing for the healthcare and engineering sector. Int J Speech Technol 2021;24:581-92. [DOI: 10.1007/s10772-020-09783-y] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
31 Birnbaum ML, Kulkarni PP, Van Meter A, Chen V, Rizvi AF, Arenare E, De Choudhury M, Kane JM. Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study. JMIR Ment Health 2020;7:e19348. [PMID: 32870161 DOI: 10.2196/19348] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
32 Aafjes-van Doorn K, Kamsteeg C, Bate J, Aafjes M. A scoping review of machine learning in psychotherapy research. Psychother Res 2021;31:92-116. [PMID: 32862761 DOI: 10.1080/10503307.2020.1808729] [Cited by in Crossref: 15] [Cited by in F6Publishing: 9] [Article Influence: 7.5] [Reference Citation Analysis]
33 Karasch O, Schmitz-Buhl M, Mennicken R, Zielasek J, Gouzoulis-Mayfrank E. Identification of risk factors for involuntary psychiatric hospitalization: using environmental socioeconomic data and methods of machine learning to improve prediction. BMC Psychiatry 2020;20:401. [PMID: 32770970 DOI: 10.1186/s12888-020-02803-w] [Cited by in Crossref: 3] [Cited by in F6Publishing: 6] [Article Influence: 1.5] [Reference Citation Analysis]
34 Brunn M, Diefenbacher A, Courtet P, Genieys W. The Future is Knocking: How Artificial Intelligence Will Fundamentally Change Psychiatry. Acad Psychiatry 2020;44:461-6. [PMID: 32424706 DOI: 10.1007/s40596-020-01243-8] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
35 Han D, Fang Y, Luo H. A Predictive Model Offor Attention Deficit Hyperactivity Disorder Based on Clinical Assessment Tools. Neuropsychiatr Dis Treat 2020;16:1331-7. [PMID: 32547036 DOI: 10.2147/NDT.S245636] [Reference Citation Analysis]
36 Khalique F, Khan SA, Butt WH, Matloob I. An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan. Int J Environ Res Public Health 2020;17:E3763. [PMID: 32466471 DOI: 10.3390/ijerph17113763] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
37 Zheng Y, Hu X. Healthcare predictive analytics for disease progression: a longitudinal data fusion approach. J Intell Inf Syst 2020;55:351-69. [DOI: 10.1007/s10844-020-00606-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
38 Tamura JK, Mcintyre RS. Current and Future Vistas in Bipolar Disorder. Curr Behav Neurosci Rep 2020;7:68-75. [DOI: 10.1007/s40473-020-00202-9] [Reference Citation Analysis]
39 Tandon N, Tandon R. Machine learning in psychiatry- standards and guidelines. Asian J Psychiatr 2019;44:A1-4. [PMID: 31530438 DOI: 10.1016/j.ajp.2019.09.009] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 2.7] [Reference Citation Analysis]