BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Shatte ABR, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychol Med 2019;49:1426-48. [PMID: 30744717 DOI: 10.1017/S0033291719000151] [Cited by in Crossref: 116] [Cited by in F6Publishing: 55] [Article Influence: 38.7] [Reference Citation Analysis]
Number Citing Articles
1 Flanagan O, Chan A, Roop P, Sundram F. Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review. JMIR Mhealth Uhealth 2021;9:e24352. [PMID: 34533465 DOI: 10.2196/24352] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Lenze EJ, Nicol GE, Barbour DL, Kannampallil T, Wong AWK, Piccirillo J, Drysdale AT, Sylvester CM, Haddad R, Miller JP, Low CA, Lenze SN, Freedland KE, Rodebaugh TL. Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders. J Psychiatry Neurosci 2021;46:E97-E110. [PMID: 33206039 DOI: 10.1503/jpn.200042] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
3 Sharma A, Verbeke WJMI. Improving Diagnosis of Depression With XGBOOST Machine Learning Model and a Large Biomarkers Dutch Dataset (n = 11,081). Front Big Data 2020;3:15. [PMID: 33693389 DOI: 10.3389/fdata.2020.00015] [Cited by in Crossref: 12] [Cited by in F6Publishing: 2] [Article Influence: 6.0] [Reference Citation Analysis]
4 Cuzzolin F, Morelli A, Cîrstea B, Sahakian BJ. Knowing me, knowing you: theory of mind in AI. Psychol Med 2020;50:1057-61. [PMID: 32375908 DOI: 10.1017/S0033291720000835] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
5 Ren Y, Lu C, Yang H, Ma Q, Barnhart WR, Zhou J, He J. Using machine learning to explore core risk factors associated with the risk of eating disorders among non-clinical young women in China: A decision-tree classification analysis. J Eat Disord 2022;10:19. [PMID: 35144682 DOI: 10.1186/s40337-022-00545-6] [Reference Citation Analysis]
6 Bickman L. Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health. Adm Policy Ment Health 2020;47:795-843. [PMID: 32715427 DOI: 10.1007/s10488-020-01065-8] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 11.0] [Reference Citation Analysis]
7 Laacke S, Mueller R, Schomerus G, Salloch S. Artificial Intelligence, Social Media and Depression. A New Concept of Health-Related Digital Autonomy. Am J Bioeth 2021;21:4-20. [PMID: 33393864 DOI: 10.1080/15265161.2020.1863515] [Cited by in Crossref: 16] [Cited by in F6Publishing: 15] [Article Influence: 16.0] [Reference Citation Analysis]
8 Kim J, Lee D, Park E. Machine Learning for Mental Health in Social Media: Bibliometric Study. J Med Internet Res 2021;23:e24870. [PMID: 33683209 DOI: 10.2196/24870] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
9 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]
10 Zhang W, Liu H, Silenzio VMB, Qiu P, Gong W. Machine Learning Models for the Prediction of Postpartum Depression: Application and Comparison Based on a Cohort Study. JMIR Med Inform 2020;8:e15516. [PMID: 32352387 DOI: 10.2196/15516] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 4.5] [Reference Citation Analysis]
11 Corsico P. "It's all about delivery": researchers and health professionals' views on the moral challenges of accessing neurobiological information in the context of psychosis. BMC Med Ethics 2021;22:11. [PMID: 33557813 DOI: 10.1186/s12910-020-00551-w] [Reference Citation Analysis]
12 Shi N, Zhang D, Li L, Xu S. Predicting Mental Health Problems with Automatic Identification of Metaphors. J Healthc Eng 2021;2021:5582714. [PMID: 34012545 DOI: 10.1155/2021/5582714] [Reference Citation Analysis]
13 Jacobson NC, Bentley KH, Walton A, Wang SB, Fortgang RG, Millner AJ, Coombs G 3rd, Rodman AM, Coppersmith DDL. Ethical dilemmas posed by mobile health and machine learning in psychiatry research. Bull World Health Organ 2020;98:270-6. [PMID: 32284651 DOI: 10.2471/BLT.19.237107] [Cited by in Crossref: 10] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
14 Kamath J, Bi J, Russell A, Wang B. Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics. J Psychiatr Brain Sci 2020;5:e200010. [PMID: 32529036 DOI: 10.20900/jpbs.20200010] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
15 Liu D, Feng XL, Ahmed F, Shahid M, Guo J. Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review. JMIR Ment Health 2022;9:e27244. [PMID: 35230252 DOI: 10.2196/27244] [Reference Citation Analysis]
16 Schroeder J, Suh J, Wilks C, Czerwinski M, Munson SA, Fogarty J, Althoff T. Data-Driven Implications for Translating Evidence-Based Psychotherapies into Technology-Delivered Interventions. Int Conf Pervasive Comput Technol Healthc 2020;2020:274-87. [PMID: 33912357 DOI: 10.1145/3421937.3421975] [Reference Citation Analysis]
17 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]
18 Keith J, Williams M, Taravath S, Lecci L. A Clinician’s Guide to Machine Learning in Neuropsychological Research and Practice. J Pediatr Neuropsychol 2019;5:177-87. [DOI: 10.1007/s40817-019-00075-1] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
19 Jacob D, Unnsteinsdóttir Kristensen IS, Aubonnet R, Recenti M, Donisi L, Ricciardi C, Svansson HÁR, Agnarsdóttir S, Colacino A, Jónsdóttir MK, Kristjánsdóttir H, Sigurjónsdóttir HÁ, Cesarelli M, Eggertsdóttir Claessen LÓ, Hassan M, Petersen H, Gargiulo P. Towards defining biomarkers to evaluate concussions using virtual reality and a moving platform (BioVRSea). Sci Rep 2022;12. [DOI: 10.1038/s41598-022-12822-0] [Reference Citation Analysis]
20 Taylor AK, Steeg S, Quinlivan L, Gunnell D, Hawton K, Kapur N. Accuracy of individual and combined risk-scale items in the prediction of repetition of self-harm: multicentre prospective cohort study. BJPsych Open 2020;7:e2. [PMID: 33261707 DOI: 10.1192/bjo.2020.123] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
21 Richter T, Fishbain B, Richter-Levin G, Okon-Singer H. Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions. J Pers Med 2021;11:957. [PMID: 34683098 DOI: 10.3390/jpm11100957] [Reference Citation Analysis]
22 Javidan AP, Li A, Lee MH, Forbes TL, Naji F. A Systematic Review and Bibliometric Analysis of Applications of Artificial Intelligence and Machine Learning in Vascular Surgery. Ann Vasc Surg 2022:S0890-5096(22)00148-0. [PMID: 35339595 DOI: 10.1016/j.avsg.2022.03.019] [Reference Citation Analysis]
23 Le Glaz A, Haralambous Y, Kim-Dufor DH, Lenca P, Billot R, Ryan TC, Marsh J, DeVylder J, Walter M, Berrouiguet S, Lemey C. Machine Learning and Natural Language Processing in Mental Health: Systematic Review. J Med Internet Res 2021;23:e15708. [PMID: 33944788 DOI: 10.2196/15708] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
24 Gyllenberg D, McKeague IW, Sourander A, Brown AS. Robust data-driven identification of risk factors and their interactions: A simulation and a study of parental and demographic risk factors for schizophrenia. Int J Methods Psychiatr Res 2020;29:1-11. [PMID: 32520440 DOI: 10.1002/mpr.1834] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
25 Naslund JA, Gonsalves PP, Gruebner O, Pendse SR, Smith SL, Sharma A, Raviola G. Digital Innovations for Global Mental Health: Opportunities for Data Science, Task Sharing, and Early Intervention. Curr Treat Options Psychiatry 2019;6:337-51. [PMID: 32457823 DOI: 10.1007/s40501-019-00186-8] [Cited by in Crossref: 19] [Cited by in F6Publishing: 11] [Article Influence: 6.3] [Reference Citation Analysis]
26 Ophir Y, Tikochinski R, Asterhan CSC, Sisso I, Reichart R. Deep neural networks detect suicide risk from textual facebook posts. Sci Rep 2020;10:16685. [PMID: 33028921 DOI: 10.1038/s41598-020-73917-0] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
27 Leeds DD, Zeng Y, Johnson BR, Foster CA, D'Lauro C. Beliefs affecting concussion reporting among military cadets: advanced observations through machine learning. Brain Inj 2022;:1-10. [PMID: 35133926 DOI: 10.1080/02699052.2022.2034945] [Reference Citation Analysis]
28 Goh YS, Ow Yong JQY, Chee BQH, Kuek JHL, Ho CSH. Machine Learning in Health Promotion and Behavioral Change: Scoping Review. J Med Internet Res 2022;24:e35831. [PMID: 35653177 DOI: 10.2196/35831] [Reference Citation Analysis]
29 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] [Article Influence: 1.0] [Reference Citation Analysis]
30 Saqib K, Khan AF, Butt ZA. Machine Learning Methods for Predicting Postpartum Depression: Scoping Review. JMIR Ment Health 2021;8:e29838. [PMID: 34822337 DOI: 10.2196/29838] [Reference Citation Analysis]
31 Zhu L, Bin S. Work Emotion Intervention and Guidance Training Method for Enterprise Employees Based on Virtual Reality. Occupational Therapy International 2022;2022:1-13. [DOI: 10.1155/2022/3909734] [Reference Citation Analysis]
32 Greenwood CJ, Youssef GJ, Letcher P, Macdonald JA, Hagg LJ, Sanson A, Mcintosh J, Hutchinson DM, Toumbourou JW, Fuller-Tyszkiewicz M, Olsson CA. A comparison of penalised regression methods for informing the selection of predictive markers. PLoS One 2020;15:e0242730. [PMID: 33216811 DOI: 10.1371/journal.pone.0242730] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
33 Alzubaidi MS, Shah U, Dhia Zubaydi H, Dolaat K, Abd-Alrazaq AA, Ahmed A, Househ M. The Role of Neural Network for the Detection of Parkinson's Disease: A Scoping Review. Healthcare (Basel) 2021;9:740. [PMID: 34208654 DOI: 10.3390/healthcare9060740] [Reference Citation Analysis]
34 Noor MBT, Zenia NZ, Kaiser MS, Mamun SA, Mahmud M. Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer's disease, Parkinson's disease and schizophrenia. Brain Inform 2020;7:11. [PMID: 33034769 DOI: 10.1186/s40708-020-00112-2] [Cited by in Crossref: 42] [Cited by in F6Publishing: 10] [Article Influence: 21.0] [Reference Citation Analysis]
35 Dobias ML, Sugarman MB, Mullarkey MC, Schleider JL. Predicting Mental Health Treatment Access Among Adolescents With Elevated Depressive Symptoms: Machine Learning Approaches. Adm Policy Ment Health 2021. [PMID: 34213666 DOI: 10.1007/s10488-021-01146-2] [Reference Citation Analysis]
36 Vélez JI. Machine Learning based Psychology: Advocating for A Data-Driven Approach. Int J Psychol Res (Medellin) 2021;14:6-11. [PMID: 34306575 DOI: 10.21500/20112084.5365] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
37 Deng X, Sun G. A Fuzzy Qualitative Simulation Study of College Student’s Mental Health Status. Discrete Dynamics in Nature and Society 2022;2022:1-10. [DOI: 10.1155/2022/5177969] [Reference Citation Analysis]
38 Ćosić K, Popović S, Šarlija M, Kesedžić I, Gambiraža M, Dropuljić B, Mijić I, Henigsberg N, Jovanovic T. AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients. Front Psychol 2021;12:782866. [PMID: 35027902 DOI: 10.3389/fpsyg.2021.782866] [Reference Citation Analysis]
39 Goldberg SB, Flemotomos N, Martinez VR, Tanana MJ, Kuo PB, Pace BT, Villatte JL, Georgiou PG, Van Epps J, Imel ZE, Narayanan SS, Atkins DC. Machine learning and natural language processing in psychotherapy research: Alliance as example use case. J Couns Psychol 2020;67:438-48. [PMID: 32614225 DOI: 10.1037/cou0000382] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 4.0] [Reference Citation Analysis]
40 Arora A, Chakraborty P, Bhatia MPS. Intervention of Wearables and Smartphones in Real Time Monitoring of Sleep and Behavioral Health: An Assessment Using Adaptive Neuro-Fuzzy Technique. Arab J Sci Eng 2022;47:1999-2024. [DOI: 10.1007/s13369-021-06078-5] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
41 Aebi NJ, De Ridder D, Ochoa C, Petrovic D, Fadda M, Elayan S, Sykora M, Puhan M, Naslund JA, Mooney SJ, Gruebner O. Can Big Data Be Used to Monitor the Mental Health Consequences of COVID-19? Int J Public Health 2021;66:633451. [PMID: 34744586 DOI: 10.3389/ijph.2021.633451] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
42 Plessas A, Espinosa-Ramos JI, Parry D, Cowie S, Landon J. Machine learning with a snapshot of data: Spiking neural network 'predicts' reinforcement histories of pigeons' choice behavior. J Exp Anal Behav 2022. [PMID: 35445745 DOI: 10.1002/jeab.759] [Reference Citation Analysis]
43 Macalli M, Navarro M, Orri M, Tournier M, Thiébaut R, Côté SM, Tzourio C. A machine learning approach for predicting suicidal thoughts and behaviours among college students. Sci Rep 2021;11:11363. [PMID: 34131161 DOI: 10.1038/s41598-021-90728-z] [Reference Citation Analysis]
44 Balcombe L, De Leo D. Human-Computer Interaction in Digital Mental Health. Informatics 2022;9:14. [DOI: 10.3390/informatics9010014] [Reference Citation Analysis]
45 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: 29] [Article Influence: 16.0] [Reference Citation Analysis]
46 Sharma A, Verbeke WJMI. Understanding importance of clinical biomarkers for diagnosis of anxiety disorders using machine learning models. PLoS One 2021;16:e0251365. [PMID: 33970950 DOI: 10.1371/journal.pone.0251365] [Reference Citation Analysis]
47 Vélez JI, Samper LA, Arcos-Holzinger M, Espinosa LG, Isaza-Ruget MA, Lopera F, Arcos-Burgos M. A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer's Disease. Diagnostics (Basel) 2021;11:887. [PMID: 34067584 DOI: 10.3390/diagnostics11050887] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
48 Moshe I, Terhorst Y, Opoku Asare K, Sander LB, Ferreira D, Baumeister H, Mohr DC, Pulkki-Råback L. Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data. Front Psychiatry 2021;12:625247. [PMID: 33584388 DOI: 10.3389/fpsyt.2021.625247] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
49 Augsburger M, Galatzer-Levy IR. Utilization of machine learning to test the impact of cognitive processing and emotion recognition on the development of PTSD following trauma exposure. BMC Psychiatry 2020;20:325. [PMID: 32576245 DOI: 10.1186/s12888-020-02728-4] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
50 Corsico P. Psychosis, vulnerability, and the moral significance of biomedical innovation in psychiatry. Why ethicists should join efforts. Med Health Care Philos 2020;23:269-79. [PMID: 31773383 DOI: 10.1007/s11019-019-09932-4] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
51 Li L, Arif M. Application of Image Denoising Algorithm and Data Mining in Psychological Teaching Quality Evaluation. Security and Communication Networks 2022;2022:1-12. [DOI: 10.1155/2022/4172887] [Reference Citation Analysis]
52 Baminiwatta A. Global trends of machine learning applications in psychiatric research over 30 years: A bibliometric analysis. Asian J Psychiatr 2021;69:102986. [PMID: 34990914 DOI: 10.1016/j.ajp.2021.102986] [Reference Citation Analysis]
53 Borges do Nascimento IJ, Marcolino MS, Abdulazeem HM, Weerasekara I, Azzopardi-Muscat N, Gonçalves MA, Novillo-Ortiz D. Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies. J Med Internet Res 2021;23:e27275. [PMID: 33847586 DOI: 10.2196/27275] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
54 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]
55 Noor NSEM, Ibrahim H, Lah MHC, Abdullah JM. Improving Outcome Prediction for Traumatic Brain Injury From Imbalanced Datasets Using RUSBoosted Trees on Electroencephalography Spectral Power. IEEE Access 2021;9:121608-31. [DOI: 10.1109/access.2021.3109780] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
56 Rodríguez-ruiz JG, Galván-tejada CE, Vázquez-reyes S, Galván-tejada JI, Gamboa-rosales H. Classification of Depressive Episodes Using Nighttime Data; a Multivariate and Univariate Analysis. Program Comput Soft 2020;46:689-98. [DOI: 10.1134/s0361768820080198] [Reference Citation Analysis]
57 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]
58 Wies B, Landers C, Ienca M. Digital Mental Health for Young People: A Scoping Review of Ethical Promises and Challenges. Front Digit Health 2021;3:697072. [PMID: 34713173 DOI: 10.3389/fdgth.2021.697072] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
59 Bettis AH, Burke TA, Nesi J, Liu RT. Digital Technologies for Emotion-Regulation Assessment and Intervention: A Conceptual Review. Clinical Psychological Science 2022;10:3-26. [DOI: 10.1177/21677026211011982] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
60 Kundu A, Chaiton M, Billington R, Grace D, Fu R, Logie C, Baskerville B, Yager C, Mitsakakis N, Schwartz R. Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review. JMIR Med Inform 2021;9:e28962. [PMID: 34762059 DOI: 10.2196/28962] [Reference Citation Analysis]
61 Parisi L, Ma R, Ravichandran N, Lanzillotta M. hyper-sinh: An accurate and reliable function from shallow to deep learning in TensorFlow and Keras. Machine Learning with Applications 2021;6:100112. [DOI: 10.1016/j.mlwa.2021.100112] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
62 Wang H, Shan Y, Zeng W. A Machine-Assisted Gaze Analysis Method for Students’ Psychological Evaluation. Journal of Sensors 2022;2022:1-12. [DOI: 10.1155/2022/3206444] [Reference Citation Analysis]
63 Kwak S, Oh DJ, Jeon YJ, Oh DY, Park SM, Kim H, Lee JY. Utility of Machine Learning Approach with Neuropsychological Tests in Predicting Functional Impairment of Alzheimer's Disease. J Alzheimers Dis 2021. [PMID: 34924390 DOI: 10.3233/JAD-215244] [Reference Citation Analysis]
64 Torous J, Bucci S, Bell IH, Kessing LV, Faurholt-Jepsen M, Whelan P, Carvalho AF, Keshavan M, Linardon J, Firth J. The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry 2021;20:318-35. [PMID: 34505369 DOI: 10.1002/wps.20883] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
65 Sedlakova J, Trachsel M. Conversational Artificial Intelligence in Psychotherapy: A New Therapeutic Tool or Agent? Am J Bioeth 2022;:1-10. [PMID: 35362368 DOI: 10.1080/15265161.2022.2048739] [Reference Citation Analysis]
66 Naslund JA, Bondre A, Torous J, Aschbrenner KA. Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice. J Technol Behav Sci 2020;5:245-57. [PMID: 33415185 DOI: 10.1007/s41347-020-00134-x] [Cited by in Crossref: 30] [Cited by in F6Publishing: 17] [Article Influence: 15.0] [Reference Citation Analysis]
67 Naqishbandi TA, Gulzar Z, Dar AA, Farooq U. Understanding Impact of Communication Ban on Mental Health in Conflict Zone:- Experiences from Young Kashmiri Research Scholars. Journal of Loss and Trauma 2022;27:95-8. [DOI: 10.1080/15325024.2021.1928841] [Reference Citation Analysis]
68 Lekkas D, Jacobson NC. Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma. Sci Rep 2021;11:10303. [PMID: 33986445 DOI: 10.1038/s41598-021-89768-2] [Reference Citation Analysis]
69 Gutierrez LJ, Rabbani K, Ajayi OJ, Gebresilassie SK, Rafferty J, Castro LA, Banos O. Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management. Int J Environ Res Public Health 2021;18:1327. [PMID: 33535714 DOI: 10.3390/ijerph18031327] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
70 Guo D, Guo Y, Xing Y, Ali L. Data on the Impact of Epidemic on Nursing Staff’s Mental Health in the Context of Wireless Network. Journal of Healthcare Engineering 2022;2022:1-11. [DOI: 10.1155/2022/3413815] [Reference Citation Analysis]
71 Weng JC, Lin TY, Tsai YH, Cheok MT, Chang YE, Chen VC. An Autoencoder and Machine Learning Model to Predict Suicidal Ideation with Brain Structural Imaging. J Clin Med 2020;9:E658. [PMID: 32121362 DOI: 10.3390/jcm9030658] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
72 van Mens K, Kwakernaak S, Janssen R, Cahn W, Lokkerbol J, Tiemens B. Predicting Future Service Use in Dutch Mental Healthcare: A Machine Learning Approach. Adm Policy Ment Health 2021. [PMID: 34463857 DOI: 10.1007/s10488-021-01150-6] [Reference Citation Analysis]
73 Yang B. Model innovation of students' mental health education from the perspective of big data. Expert Systems. [DOI: 10.1111/exsy.12948] [Reference Citation Analysis]
74 Ray A, Bhardwaj A, Malik YK, Singh S, Gupta R. Artificial intelligence and Psychiatry: An overview. Asian Journal of Psychiatry 2022. [DOI: 10.1016/j.ajp.2022.103021] [Reference Citation Analysis]
75 Tao X, Chi O, Delaney PJ, Li L, Huang J. Detecting depression using an ensemble classifier based on Quality of Life scales. Brain Inform 2021;8:2. [PMID: 33590388 DOI: 10.1186/s40708-021-00125-5] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
76 Hassani H, Huang X, Macfeely S, Entezarian MR. Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance. BDCC 2021;5:28. [DOI: 10.3390/bdcc5030028] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
77 Cohen J, Wright-berryman J, Rohlfs L, Trocinski D, Daniel L, Klatt TW. Integration and Validation of a Natural Language Processing Machine Learning Suicide Risk Prediction Model Based on Open-Ended Interview Language in the Emergency Department. Front Digit Health 2022;4:818705. [DOI: 10.3389/fdgth.2022.818705] [Reference Citation Analysis]
78 Shatte ABR, Teague SJ. schema: an open-source, distributed mobile platform for deploying mHealth research tools and interventions. BMC Med Res Methodol 2020;20:91. [PMID: 32334522 DOI: 10.1186/s12874-020-00973-5] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
79 Movaghar A, Page D, Saha K, Rynn M, Greenberg J. Machine learning approach to measurement of criticism: The core dimension of expressed emotion. J Fam Psychol 2021;35:1007-15. [PMID: 34410788 DOI: 10.1037/fam0000906] [Reference Citation Analysis]
80 Fu Q, Jing Y, Liu Mr G, Jiang Mr X, Liu H, Kong Y, Hou X, Cao L, Deng P, Xiao P, Xiao J, Peng H, Wei X. Machine learning-based method for tacrolimus dose predictions in Chinese kidney transplant perioperative patients. J Clin Pharm Ther 2021. [PMID: 34802160 DOI: 10.1111/jcpt.13579] [Reference Citation Analysis]
81 Dawoodbhoy FM, Delaney J, Cecula P, Yu J, Peacock I, Tan J, Cox B. AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units. Heliyon 2021;7:e06993. [PMID: 34036191 DOI: 10.1016/j.heliyon.2021.e06993] [Reference Citation Analysis]
82 Shaikh SG, Suresh Kumar B, Narang G. Recommender system for health care analysis using machine learning technique: a review. Theoretical Issues in Ergonomics Science. [DOI: 10.1080/1463922x.2022.2061078] [Reference Citation Analysis]
83 Navarro MC, Ouellet-Morin I, Geoffroy MC, Boivin M, Tremblay RE, Côté SM, Orri M. Machine Learning Assessment of Early Life Factors Predicting Suicide Attempt in Adolescence or Young Adulthood. JAMA Netw Open 2021;4:e211450. [PMID: 33710292 DOI: 10.1001/jamanetworkopen.2021.1450] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
84 Afshar M, Joyce C, Dligach D, Sharma B, Kania R, Xie M, Swope K, Salisbury-Afshar E, Karnik NS. Subtypes in patients with opioid misuse: A prognostic enrichment strategy using electronic health record data in hospitalized patients. PLoS One 2019;14:e0219717. [PMID: 31310611 DOI: 10.1371/journal.pone.0219717] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 3.7] [Reference Citation Analysis]
85 Zhu X, Huang W, Lu H, Wang Z, Ni X, Hu J, Deng S, Tan Y, Li L, Zhang M, Qiu C, Luo Y, Chen H, Huang S, Xiao T, Shang D, Wen Y. A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters. Sci Rep 2021;11:5568. [PMID: 33692435 DOI: 10.1038/s41598-021-85157-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
86 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]
87 Cecula P, Yu J, Dawoodbhoy FM, Delaney J, Tan J, Peacock I, Cox B. Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review. Heliyon 2021;7:e06626. [PMID: 33898804 DOI: 10.1016/j.heliyon.2021.e06626] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
88 Ignatiev NA, Stankevich MA, Smirnov IV, Kiselnikova NV, Grigoriev OG. Predicting Personal Traits from Vkontakte Images. Sci Tech Inf Proc 2020;47:383-8. [DOI: 10.3103/s0147688220060039] [Reference Citation Analysis]
89 Zirikly A, Desmet B, Newman-griffis D, Marfeo E, Mcdonough C, Goldman H, Chan L. Viewpoint: An Information Extraction Framework for Disability Determination Using a Mental Functioning Use-Case (Preprint). JMIR Medical Informatics. [DOI: 10.2196/32245] [Reference Citation Analysis]
90 Su C, Xu Z, Pathak J, Wang F. Deep learning in mental health outcome research: a scoping review. Transl Psychiatry 2020;10:116. [PMID: 32532967 DOI: 10.1038/s41398-020-0780-3] [Cited by in Crossref: 16] [Cited by in F6Publishing: 10] [Article Influence: 8.0] [Reference Citation Analysis]
91 Mak ML, Al-Shaqsi SZ, Phillips J. Prevalence of Machine Learning in Craniofacial Surgery. J Craniofac Surg 2020;31:898-903. [PMID: 32168124 DOI: 10.1097/SCS.0000000000006234] [Reference Citation Analysis]
92 Bondre A, Pathare S, Naslund JA. Protecting Mental Health Data Privacy in India: The Case of Data Linkage With Aadhaar. Glob Health Sci Pract 2021;9:467-80. [PMID: 34593574 DOI: 10.9745/GHSP-D-20-00346] [Reference Citation Analysis]
93 Fuller-Tyszkiewicz M, Richardson B, Little K, Teague S, Hartley-Clark L, Capic T, Khor S, Cummins RA, Olsson CA, Hutchinson D. Efficacy of a Smartphone App Intervention for Reducing Caregiver Stress: Randomized Controlled Trial. JMIR Ment Health 2020;7:e17541. [PMID: 32706716 DOI: 10.2196/17541] [Cited by in Crossref: 10] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
94 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]
95 Chen X, Pan Z. A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health. Int J Environ Res Public Health 2021;18:6441. [PMID: 34198659 DOI: 10.3390/ijerph18126441] [Reference Citation Analysis]
96 Cho SE, Geem ZW, Na KS. Predicting Depression in Community Dwellers Using a Machine Learning Algorithm. Diagnostics (Basel) 2021;11:1429. [PMID: 34441363 DOI: 10.3390/diagnostics11081429] [Reference Citation Analysis]
97 Zhou S, Zhao J, Zhang L. Application of Artificial Intelligence on Psychological Interventions and Diagnosis: An Overview. Front Psychiatry 2022;13:811665. [DOI: 10.3389/fpsyt.2022.811665] [Reference Citation Analysis]
98 Edo-Osagie O, De La Iglesia B, Lake I, Edeghere O. A scoping review of the use of Twitter for public health research. Comput Biol Med 2020;122:103770. [PMID: 32502758 DOI: 10.1016/j.compbiomed.2020.103770] [Cited by in Crossref: 16] [Cited by in F6Publishing: 7] [Article Influence: 8.0] [Reference Citation Analysis]
99 Teague SJ, Shatte ABR, Weller E, Fuller-Tyszkiewicz M, Hutchinson DM. Methods and Applications of Social Media Monitoring of Mental Health During Disasters: Scoping Review. JMIR Ment Health 2022;9:e33058. [PMID: 35225815 DOI: 10.2196/33058] [Reference Citation Analysis]
100 Jilka S, Odoi CM, van Bilsen J, Morris D, Erturk S, Cummins N, Cella M, Wykes T. Identifying schizophrenia stigma on Twitter: a proof of principle model using service user supervised machine learning. npj Schizophr 2022;8. [DOI: 10.1038/s41537-021-00197-6] [Reference Citation Analysis]
101 Pankajavalli PB, Karthick GS, Sakthivel R. An Efficient Machine Learning Framework for Stress Prediction via Sensor Integrated Keyboard Data. IEEE Access 2021;9:95023-35. [DOI: 10.1109/access.2021.3094334] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
102 Balcombe L, De Leo D. Digital Mental Health Challenges and the Horizon Ahead for Solutions. JMIR Ment Health 2021;8:e26811. [PMID: 33779570 DOI: 10.2196/26811] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
103 Jiang T, Gradus JL, Rosellini AJ. Supervised Machine Learning: A Brief Primer. Behav Ther 2020;51:675-87. [PMID: 32800297 DOI: 10.1016/j.beth.2020.05.002] [Cited by in Crossref: 15] [Cited by in F6Publishing: 8] [Article Influence: 7.5] [Reference Citation Analysis]
104 Li L, Diouf F, Gorkhali A. Managing outpatient flow via an artificial intelligence enabled solution. Syst Res Behav Sci. [DOI: 10.1002/sres.2870] [Reference Citation Analysis]
105 Thieme A, Belgrave D, Doherty G. Machine Learning in Mental Health: A Systematic Review of the HCI Literature to Support the Development of Effective and Implementable ML Systems. ACM Trans Comput -Hum Interact 2020;27:1-53. [DOI: 10.1145/3398069] [Cited by in Crossref: 24] [Cited by in F6Publishing: 2] [Article Influence: 12.0] [Reference Citation Analysis]
106 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] [Article Influence: 1.0] [Reference Citation Analysis]