Published on 16.05.16 in Vol 3, No 2 (2016): Apr-Jun
Works citing "Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality"
According to Crossref, the following articles are citing this article (DOI 10.2196/mental.4822):
(note that this is only a small subset of citations)
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Ortiz P, Khin Khin E. Traditional and new media's influence on suicidal behavior and contagion. Behavioral Sciences & the Law 2018;36(2):245
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Carreiro S, Chai PR, Carey J, Chapman B, Boyer EW. Integrating Personalized Technology in Toxicology: Sensors, Smart Glass, and Social Media Applications in Toxicology Research. Journal of Medical Toxicology 2017;13(2):166
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Paul MJ, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1
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Chancellor S, De Choudhury M. Methods in predictive techniques for mental health status on social media: a critical review. npj Digital Medicine 2020;3(1)
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Soucy JN, Hadjistavropoulos HD, Couture CA, Owens VA, Dear BF, Titov N. Content of client emails in internet-delivered cognitive behaviour therapy: A comparison between two trials and relationship to client outcome. Internet Interventions 2018;11:53
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Grant RN, Kucher D, León AM, Gemmell JF, Raicu DS, Fodeh SJ. Automatic extraction of informal topics from online suicidal ideation. BMC Bioinformatics 2018;19(S8)
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Bernert RA, Hilberg AM, Melia R, Kim JP, Shah NH, Abnousi F. Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations. International Journal of Environmental Research and Public Health 2020;17(16):5929
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Chancellor S, Baumer EPS, De Choudhury M. Who is the "Human" in Human-Centered Machine Learning. Proceedings of the ACM on Human-Computer Interaction 2019;3(CSCW):1
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Colditz JB, Chu K, Emery SL, Larkin CR, James AE, Welling J, Primack BA. Toward Real-Time Infoveillance of Twitter Health Messages. American Journal of Public Health 2018;108(8):1009
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Roy A, Nikolitch K, McGinn R, Jinah S, Klement W, Kaminsky ZA. A machine learning approach predicts future risk to suicidal ideation from social media data. npj Digital Medicine 2020;3(1)
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Yin Z, Sulieman LM, Malin BA. A systematic literature review of machine learning in online personal health data. Journal of the American Medical Informatics Association 2019;26(6):561
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Glenn JJ, Nobles AL, Barnes LE, Teachman BA. Can Text Messages Identify Suicide Risk in Real Time? A Within-Subjects Pilot Examination of Temporally Sensitive Markers of Suicide Risk. Clinical Psychological Science 2020;8(4):704
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Spates K, Ye X, Johnson A. “I just might kill myself”: Suicide expressions on Twitter. Death Studies 2020;44(3):189
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Cohan A, Young S, Yates A, Goharian N. Triaging content severity in online mental health forums. Journal of the Association for Information Science and Technology 2017;68(11):2675
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Coşkun M, Ozturan M. #europehappinessmap: A Framework for Multi-Lingual Sentiment Analysis via Social Media Big Data (A Twitter Case Study). Information 2018;9(5):102
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Walters K, Christakis DA, Wright DR, Alamian A. Are Mechanical Turk worker samples representative of health status and health behaviors in the U.S.?. PLOS ONE 2018;13(6):e0198835
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Oexle N, Niederkrotenthaler T, DeLeo D. Emerging trends in suicide prevention research. Current Opinion in Psychiatry 2019;32(4):336
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Bruen AJ, Wall A, Haines-Delmont A, Perkins E. Exploring Suicidal Ideation Using an Innovative Mobile App-Strength Within Me: The Usability and Acceptability of Setting up a Trial Involving Mobile Technology and Mental Health Service Users. JMIR Mental Health 2020;7(9):e18407
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Priya A, Garg S, Tigga NP. Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms. Procedia Computer Science 2020;167:1258
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Brown RC, Bendig E, Fischer T, Goldwich AD, Baumeister H, Plener PL, Harris KM. Can acute suicidality be predicted by Instagram data? Results from qualitative and quantitative language analyses. PLOS ONE 2019;14(9):e0220623
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Aladağ AE, Muderrisoglu S, Akbas NB, Zahmacioglu O, Bingol HO. Detecting Suicidal Ideation on Forums: Proof-of-Concept Study. Journal of Medical Internet Research 2018;20(6):e215
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Tadesse MM, Lin H, Xu B, Yang L. Detection of Suicide Ideation in Social Media Forums Using Deep Learning. Algorithms 2019;13(1):7
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Barnes M, Hanson C, Giraud-Carrier C. The Case for Computational Health Science. Journal of Healthcare Informatics Research 2018;2(1-2):99
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Haines-Delmont A, Chahal G, Bruen AJ, Wall A, Khan CT, Sadashiv R, Fearnley D. Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study. JMIR mHealth and uHealth 2020;8(6):e15901
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. Commentary: A time to reap and a time to sow: reducing the adolescent suicide rate now and in the future: commentary on Cha et al. (2018). Journal of Child Psychology and Psychiatry 2018;59(4):483
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Lopez‐Castroman J, Moulahi B, Azé J, Bringay S, Deninotti J, Guillaume S, Baca‐Garcia E. Mining social networks to improve suicide prevention: A scoping review. Journal of Neuroscience Research 2020;98(4):616
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Wongkoblap A, Vadillo MA, Curcin V. Researching Mental Health Disorders in the Era of Social Media: Systematic Review. Journal of Medical Internet Research 2017;19(6):e228
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Niederkrotenthaler T, Till B, Garcia D. Celebrity suicide on Twitter: Activity, content and network analysis related to the death of Swedish DJ Tim Bergling alias Avicii. Journal of Affective Disorders 2019;245:848
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King CA, Arango A, Ewell Foster C. Emerging trends in adolescent suicide prevention research. Current Opinion in Psychology 2018;22:89
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. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206
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Burnap P, Colombo G, Amery R, Hodorog A, Scourfield J. Multi-class machine classification of suicide-related communication on Twitter. Online Social Networks and Media 2017;2:32
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Chiang C, Kasunic A, Savage S. Crowd Coach. Proceedings of the ACM on Human-Computer Interaction 2018;2(CSCW):1
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Kornfield R, Sarma PK, Shah DV, McTavish F, Landucci G, Pe-Romashko K, Gustafson DH. Detecting Recovery Problems Just in Time: Application of Automated Linguistic Analysis and Supervised Machine Learning to an Online Substance Abuse Forum. Journal of Medical Internet Research 2018;20(6):e10136
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Melia R, Francis K, Duggan J, Bogue J, O'Sullivan M, Chambers D, Young K. Mobile Health Technology Interventions for Suicide Prevention: Protocol for a Systematic Review and Meta-Analysis. JMIR Research Protocols 2018;7(1):e28
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Burke TA, Ammerman BA, Jacobucci R. The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review. Journal of Affective Disorders 2019;245:869
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Sabus C, Johns B, Schultz N, Gagnon K. Exploration of Content and Reach of Physical Therapy-Related Discussion on Twitter. Physical Therapy 2019;99(8):1048
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Skaik R, Inkpen D. Using Social Media for Mental Health Surveillance. ACM Computing Surveys 2021;53(6):1
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Kruzan KP, Whitlock J, Bazarova NN. Examining the Relationship Between the Use of a Mobile Peer-Support App and Self-Injury Outcomes: Longitudinal Mixed Methods Study. JMIR Mental Health 2021;8(1):e21854
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Weintraub MJ, Posta F, Arevian AC, Miklowitz DJ. Using machine learning analyses of speech to classify levels of expressed emotion in parents of youth with mood disorders. Journal of Psychiatric Research 2021;136:39
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Rassy J, Bardon C, Dargis L, Côté L, Corthésy-Blondin L, Mörch C, Labelle R. Information and Communication Technology Use in Suicide Prevention: Scoping Review. Journal of Medical Internet Research 2021;23(5):e25288
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Tiwari PK, Sharma M, Garg P, Jain T, Verma VK, Hussain A. A Study on Sentiment Analysis of Mental Illness Using Machine Learning Techniques. IOP Conference Series: Materials Science and Engineering 2021;1099(1):012043
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Ji S, Pan S, Li X, Cambria E, Long G, Huang Z. Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications. IEEE Transactions on Computational Social Systems 2021;8(1):214
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Lossio-Ventura JA, Gonzales S, Morzan J, Alatrista-Salas H, Hernandez-Boussard T, Bian J. Evaluation of clustering and topic modeling methods over health-related tweets and emails. Artificial Intelligence in Medicine 2021;117:102096
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Lekkas D, Klein RJ, Jacobson NC. Predicting acute suicidal ideation on Instagram using ensemble machine learning models. Internet Interventions 2021;25:100424
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Gillan CM, Rutledge RB. Smartphones and the Neuroscience of Mental Health. Annual Review of Neuroscience 2021;44(1):129
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Yang T, Li F, Ji D, Liang X, Xie T, Tian S, Li B, Liang P. Fine-grained depression analysis based on Chinese micro-blog reviews. Information Processing & Management 2021;58(6):102681
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Jung W, Kim D, Nam S, Zhu Y. Suicidality Detection on Social Media Using Metadata and Text Feature Extraction and Machine Learning. Archives of Suicide Research 2023;27(1):13
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. Detecting Suicide Ideation in the Online Environment: A Survey of Methods and Challenges. IEEE Transactions on Computational Social Systems 2022;9(3):679
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Cao L, Zhang H, Feng L. Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media. IEEE Transactions on Multimedia 2022;24:87
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Aldhyani THH, Alsubari SN, Alshebami AS, Alkahtani H, Ahmed ZAT. Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models. International Journal of Environmental Research and Public Health 2022;19(19):12635
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Zhao Y, Liu D, Wan C, Liu X, Qiu X, Nie J. Find Supports for the Post about Mental Issues: More Than Semantic Matching. ACM Transactions on Asian and Low-Resource Language Information Processing 2022;21(6):1
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Homan S, Gabi M, Klee N, Bachmann S, Moser A, Duri' M, Michel S, Bertram A, Maatz A, Seiler G, Stark E, Kleim B. Linguistic features of suicidal thoughts and behaviors: A systematic review. Clinical Psychology Review 2022;95:102161
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Khazanov GK, Forbes CN, Dunn BD, Thase ME. Addressing anhedonia to increase depression treatment engagement. British Journal of Clinical Psychology 2022;61(2):255
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Adarsh V, Arun Kumar P, Lavanya V, Gangadharan G. Fair and Explainable Depression Detection in Social Media. Information Processing & Management 2023;60(1):103168
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Mishra S, Tripathy HK, Kumar Thakkar H, Garg D, Kotecha K, Pandya S. An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment. Frontiers in Public Health 2021;9
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García-Martínez C, Oliván-Blázquez B, Fabra J, Martínez-Martínez AB, Pérez-Yus MC, López-Del-Hoyo Y. Exploring the Risk of Suicide in Real Time on Spanish Twitter: Observational Study. JMIR Public Health and Surveillance 2022;8(5):e31800
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Moon NN, Mariam A, Sharmin S, Islam MM, Nur FN, Debnath N. Machine learning approach to predict the depression in job sectors in Bangladesh. Current Research in Behavioral Sciences 2021;2:100058
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Safa R, Bayat P, Moghtader L. Automatic detection of depression symptoms in twitter using multimodal analysis. The Journal of Supercomputing 2022;78(4):4709
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Yeskuatov E, Chua S, Foo LK. Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques. International Journal of Environmental Research and Public Health 2022;19(16):10347
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Kmetty Z, Bozsonyi K. Identifying Depression-Related Behavior on Facebook—An Experimental Study. Social Sciences 2022;11(3):135
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Stupinski AM, Alshaabi T, Arnold MV, Adams JL, Minot JR, Price M, Dodds PS, Danforth CM. Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study. JMIR Mental Health 2022;9(3):e33685
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Kouter K, Videtic Paska A. ‘Omics’ of suicidal behaviour: A path to personalised psychiatry. World Journal of Psychiatry 2021;11(10):774
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Islam J, Akhand MAH, Habib MA, Kamal MAS, Siddique N. Recognition of Emotion from Emoticon with Text in Microblog Using LSTM. Advances in Science, Technology and Engineering Systems Journal 2021;6(3):347
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Acuña Caicedo RW, Gómez Soriano JM, Melgar Sasieta HA. Bootstrapping semi-supervised annotation method for potential suicidal messages. Internet Interventions 2022;28:100519
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Feldhege J, Wolf M, Moessner M, Bauer S. Psycholinguistic changes in the communication of adolescent users in a suicidal ideation online community during the COVID-19 pandemic. European Child & Adolescent Psychiatry 2023;32(6):975
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Lomotey RK, Kumi S, Hilton M, Orji R, Deters R. Using Machine Learning to Establish the Concerns of Persons With HIV/AIDS During the COVID-19 Pandemic From Their Tweets. IEEE Access 2023;11:37570
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Di Cara NH, Maggio V, Davis OSP, Haworth CMA. Methodologies for Monitoring Mental Health on Twitter: Systematic Review. Journal of Medical Internet Research 2023;25:e42734
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Hilty DM, Stubbe D, McKean AJ, Hoffman PE, Zalpuri I, Myint MT, Joshi SV, Pakyurek M, Li ST. A scoping review of social media in child, adolescents and young adults: research findings in depression, anxiety and other clinical challenges. BJPsych Open 2023;9(5)
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Hasib KM, Islam MR, Sakib S, Akbar MA, Razzak I, Alam MS. Depression Detection From Social Networks Data Based on Machine Learning and Deep Learning Techniques: An Interrogative Survey. IEEE Transactions on Computational Social Systems 2023;10(4):1568
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Cao L, Zhang H, Wang X, Feng L. Learning Users Inner Thoughts and Emotion Changes for Social Media Based Suicide Risk Detection. IEEE Transactions on Affective Computing 2023;14(2):1280
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Zhao Y, Liu D, Wan C, Liu X, Nie J, Liu J. JMS-QA: A Joint Hierarchical Architecture for Mental Health Question Answering. IEEE/ACM Transactions on Audio, Speech, and Language Processing 2024;32:352
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Liu S, Sloan L, Al Baghal T, Williams M, Jessop C, Serôdio P. Linking survey with Twitter data: examining associations among smartphone usage, privacy concern and Twitter linkage consent. International Journal of Social Research Methodology 2024;:1
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Li X, Chen F, Ma L. Exploring the Potential of Artificial Intelligence in Adolescent Suicide Prevention: Current Applications, Challenges, and Future Directions. Psychiatry 2024;87(1):7
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Patel D, Sumner SA, Bowen D, Zwald M, Yard E, Wang J, Law R, Holland K, Nguyen T, Mower G, Chen Y, Johnson JI, Jespersen M, Mytty E, Lee JM, Bauer M, Caine E, De Choudhury M. Predicting state level suicide fatalities in the united states with realtime data and machine learning. npj Mental Health Research 2024;3(1)
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Kaur I, Kamini , Kaur J, Gagandeep , Singh SP, Gupta U. Enhancing explainability in predicting mental health disorders using human–machine interaction. Multimedia Tools and Applications 2024;
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Lawrence D, Carrington-Jones P, Kyron MJ. Alternatives to Suicide. 2020. :221
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Liang C, Abbott D, Hong YA, Madadi M, White A. Social Computing and Social Media. Design, Human Behavior and Analytics. 2019. Chapter 25:345
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Adrian M, Lyon AR. Technology and Adolescent Mental Health. 2018. Chapter 12:155
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Kessler RC, Bernecker SL, Bossarte RM, Luedtke AR, McCarthy JF, Nock MK, Pigeon WR, Petukhova MV, Sadikova E, VanderWeele TJ, Zuromski KL, Zaslavsky AM. Personalized Psychiatry. 2019. Chapter 5:77
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Ebert DD, Harrer M, Apolinário-Hagen J, Baumeister H. Frontiers in Psychiatry. 2019. Chapter 29:583
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Kamarudin NS, Beigi G, Manikonda L, Liu H. Open Source Intelligence and Cyber Crime. 2020. Chapter 8:195
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Koltai J, Kmetty Z, Bozsonyi K. Pathways Between Social Science and Computational Social Science. 2021. Chapter 11:237
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Sakiyama K, de Souza Rodrigues L, Matsubara ET. Intelligent Systems. 2020. Chapter 32:466
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Chanda K, Ghosh A, Dey S, Bose R, Roy S. Smart IoT for Research and Industry. 2022. Chapter 4:69
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Henry S, Yetisgen M, Uzuner O. Mental Health Informatics. 2021. Chapter 13:317
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Pramanik R, Khare S, Harshvardhan GM, Gourisaria MK. Advances in Data and Information Sciences. 2022. Chapter 21:233
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Babulal KS, Nayak BK. The Internet of Medical Things (IoMT) and Telemedicine Frameworks and Applications. 2022. chapter 12:230
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Thapa S, Ghimire A, Adhikari S, Bhoi AK, Barsocchi P. Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data. 2022. :59
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Wongkoblap A, Vadillo MA, Curcin V. Mental Health in a Digital World. 2022. :109
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Bisht SS, Shandilya H, Gupta V, Agrawal S, Jain S. Artificial Intelligence, Machine Learning, and Mental Health in Pandemics. 2022. :277
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Safa R, Edalatpanah S, Sorourkhah A. Deep Learning in Personalized Healthcare and Decision Support. 2023. :285
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Jayapal C, Yamuna SM, Manavallan S, Devasenan M. Communication and Intelligent Systems. 2023. Chapter 37:507
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Tahsin MU, Jasim S, Naheen IT. Inventive Communication and Computational Technologies. 2023. Chapter 22:327
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Whig P, Velu A, Nadikattu RR, Alkali YJ. Handbook of Computational Sciences. 2023. :245
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