Published on in Vol 19, No 6 (2017): June

Researching Mental Health Disorders in the Era of Social Media: Systematic Review

Researching Mental Health Disorders in the Era of Social Media: Systematic Review

Researching Mental Health Disorders in the Era of Social Media: Systematic Review

Journals

  1. Crocamo C, Viviani M, Bartoli F, Carrà G, Pasi G. Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter’s Users: An Exploratory Content- and Topology-Based Analysis. International Journal of Environmental Research and Public Health 2020;17(5):1510 View
  2. Malaeb D, Salameh P, Barbar S, Awad E, Haddad C, Hallit R, Sacre H, Akel M, Obeid S, Hallit S. Problematic social media use and mental health (depression, anxiety, and insomnia) among Lebanese adults: Any mediating effect of stress?. Perspectives in Psychiatric Care 2021;57(2):539 View
  3. Tran B, McIntyre R, Latkin C, Phan H, Vu G, Nguyen H, Gwee K, Ho C, Ho R. The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis. International Journal of Environmental Research and Public Health 2019;16(12):2150 View
  4. Ford E, Curlewis K, Wongkoblap A, Curcin V. Public Opinions on Using Social Media Content to Identify Users With Depression and Target Mental Health Care Advertising: Mixed Methods Survey. JMIR Mental Health 2019;6(11):e12942 View
  5. Kamiński , Łoniewski , Misera , Marlicz . Heartburn-Related Internet Searches and Trends of Interest across Six Western Countries: A Four-Year Retrospective Analysis Using Google Ads Keyword Planner. International Journal of Environmental Research and Public Health 2019;16(23):4591 View
  6. Zhou T, Hu G, Wang L. Psychological Disorder Identifying Method Based on Emotion Perception over Social Networks. International Journal of Environmental Research and Public Health 2019;16(6):953 View
  7. Notredame C, Morgiève M, Morel F, Berrouiguet S, Azé J, Vaiva G. Distress, Suicidality, and Affective Disorders at the Time of Social Networks. Current Psychiatry Reports 2019;21(10) View
  8. Choo H, Kim M, Choi J, Shin J, Shin S. Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study. Journal of Medical Internet Research 2020;22(10):e21369 View
  9. Harb J, Ebeling R, Becker K. A framework to analyze the emotional reactions to mass violent events on Twitter and influential factors. Information Processing & Management 2020;57(6):102372 View
  10. 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) View
  11. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  12. Cacheda F, Fernandez D, Novoa F, Carneiro V. Early Detection of Depression: Social Network Analysis and Random Forest Techniques. Journal of Medical Internet Research 2019;21(6):e12554 View
  13. Zhao Y, Guo Y, He X, Wu Y, Yang X, Prosperi M, Jin Y, Bian J. Assessing mental health signals among sexual and gender minorities using Twitter data. Health Informatics Journal 2020;26(2):765 View
  14. COLDER CARRAS M, MOJTABAI R, CULLEN B. Beyond Social Media: A Cross-Sectional Survey of Other Internet and Mobile Phone Applications in a Community Psychiatry Population. Journal of Psychiatric Practice 2018;24(2):127 View
  15. Alonso S, de la Torre-Díez I, Hamrioui S, López-Coronado M, Barreno D, Nozaleda L, Franco M. Data Mining Algorithms and Techniques in Mental Health: A Systematic Review. Journal of Medical Systems 2018;42(9) View
  16. Zeraatkar K, Ahmadi M. Trends of infodemiology studies: a scoping review. Health Information & Libraries Journal 2018;35(2):91 View
  17. Chancellor S, Baumer E, De Choudhury M. Who is the "Human" in Human-Centered Machine Learning. Proceedings of the ACM on Human-Computer Interaction 2019;3(CSCW):1 View
  18. Moura I, Teles A, Silva F, Viana D, Coutinho L, Barros F, Endler M. Mental health ubiquitous monitoring supported by social situation awareness: A systematic review. Journal of Biomedical Informatics 2020;107:103454 View
  19. Wang L, Liu H, Zhou T. A Sequential Emotion Approach for Diagnosing Mental Disorder on Social Media. Applied Sciences 2020;10(5):1647 View
  20. Leis A, Ronzano F, Mayer M, Furlong L, Sanz F. Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis. Journal of Medical Internet Research 2019;21(6):e14199 View
  21. Zhu B, Zheng X, Liu H, Li J, Wang P. Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics. Chaos, Solitons & Fractals 2020;140:110123 View
  22. Su C, Xu Z, Pathak J, Wang F. Deep learning in mental health outcome research: a scoping review. Translational Psychiatry 2020;10(1) View
  23. Ahmad A, Murad H. The Impact of Social Media on Panic During the COVID-19 Pandemic in Iraqi Kurdistan: Online Questionnaire Study. Journal of Medical Internet Research 2020;22(5):e19556 View
  24. Schmidt S, Kaess M. Fortschritte und Herausforderungen für die Analyse von Big Data in sozialen Medien im Jugendalter. Zeitschrift für Kinder- und Jugendpsychiatrie und Psychotherapie 2020;48(1):47 View
  25. Kumar A, Sharma A, Arora A. Anxious Depression Prediction in Real-time Social Data. SSRN Electronic Journal 2019 View
  26. Wang X, Chen S, Li T, Li W, Zhou Y, Zheng J, Chen Q, Yan J, Tang B. Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis. JMIR Medical Informatics 2020;8(7):e17958 View
  27. Huang T, Elghafari A, Relia K, Chunara R. High-resolution Temporal Representations of Alcohol and Tobacco Behaviors from Social Media Data. Proceedings of the ACM on Human-Computer Interaction 2017;1(CSCW):1 View
  28. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439 View
  29. Zhang Y, Zhang O, Li R, Flores A, Selek S, Zhang X, Xu H. Psychiatric stressor recognition from clinical notes to reveal association with suicide. Health Informatics Journal 2019;25(4):1846 View
  30. Yoo D, Birnbaum M, Van Meter A, Ali A, Arenare E, Abowd G, De Choudhury M. Designing a Clinician-Facing Tool for Using Insights From Patients’ Social Media Activity: Iterative Co-Design Approach. JMIR Mental Health 2020;7(8):e16969 View
  31. Fernández-Sotos P, Fernández-Caballero A, González P, Aparicio A, Martínez-Gras I, Torio I, Dompablo M, García-Fernández L, Santos J, Rodriguez-Jimenez R. Digital Technology for Internet Access by Patients With Early-Stage Schizophrenia in Spain: Multicenter Research Study. Journal of Medical Internet Research 2019;21(4):e11824 View
  32. Du J, Zhang Y, Luo J, Jia Y, Wei Q, Tao C, Xu H. Extracting psychiatric stressors for suicide from social media using deep learning. BMC Medical Informatics and Decision Making 2018;18(S2) View
  33. Moessner M, Feldhege J, Wolf M, Bauer S. Analyzing big data in social media: Text and network analyses of an eating disorder forum. International Journal of Eating Disorders 2018;51(7):656 View
  34. Naslund J, Gonsalves P, Gruebner O, Pendse S, Smith S, Sharma A, Raviola G. Digital Innovations for Global Mental Health: Opportunities for Data Science, Task Sharing, and Early Intervention. Current Treatment Options in Psychiatry 2019;6(4):337 View
  35. Walsh C, Chaudhry B, Dua P, Goodman K, Kaplan B, Kavuluru R, Solomonides A, Subbian V. Stigma, biomarkers, and algorithmic bias: recommendations for precision behavioral health with artificial intelligence. JAMIA Open 2020;3(1):9 View
  36. Mavragani A, Ochoa G, Tsagarakis K. Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review. Journal of Medical Internet Research 2018;20(11):e270 View
  37. Hilty D. Research Directions for Clinical Care and Technology: the JTIBS Research Column. Journal of Technology in Behavioral Science 2020;5(4):303 View
  38. Mavragani A, Ochoa G. Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis. Journal of Big Data 2018;5(1) View
  39. 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 View
  40. Resnik P, Foreman A, Kuchuk M, Musacchio Schafer K, Pinkham B. Naturally occurring language as a source of evidence in suicide prevention. Suicide and Life-Threatening Behavior 2021;51(1):88 View
  41. Skaik R, Inkpen D. Using Social Media for Mental Health Surveillance. ACM Computing Surveys 2021;53(6):1 View
  42. Ford E, Shepherd S, Jones K, Hassan L. Toward an Ethical Framework for the Text Mining of Social Media for Health Research: A Systematic Review. Frontiers in Digital Health 2021;2 View
  43. Bour C, Schmitz S, Ahne A, Perchoux C, Dessenne C, Fagherazzi G. Scoping review protocol on the use of social media for health research purposes. BMJ Open 2021;11(2):e040671 View
  44. Leis A, Ronzano F, Mayer M, Furlong L, Sanz F. Evaluating Behavioral and Linguistic Changes During Drug Treatment for Depression Using Tweets in Spanish: Pairwise Comparison Study. Journal of Medical Internet Research 2020;22(12):e20920 View
  45. Singh A, Singh J. Automation of detection of social network mental disorders – A review. IOP Conference Series: Materials Science and Engineering 2021;1022(1):012008 View
  46. Zhang W, Liu L, Cheng Q, Chen Y, Xu D, Gong W. The Relationship Between Images Posted by New Mothers on WeChat Moments and Postpartum Depression: Cohort Study. Journal of Medical Internet Research 2020;22(11):e23575 View
  47. Tao X, Chi O, Delaney P, Li L, Huang J. Detecting depression using an ensemble classifier based on Quality of Life scales. Brain Informatics 2021;8(1) View
  48. Liu S, Vahedian F, Hachen D, Lizardo O, Poellabauer C, Striegel A, Milenković T. Heterogeneous Network Approach to Predict Individuals’ Mental Health. ACM Transactions on Knowledge Discovery from Data 2021;15(2):1 View
  49. Ignatiev N, Stankevich M, Smirnov I, Kiselnikova N, Grigoriev O. Predicting Personal Traits from Vkontakte Images. Scientific and Technical Information Processing 2020;47(6):383 View
  50. Zhu Y, Cao L, Xie J, Yu Y, Chen A, Huang F. Using social media data to assess the impact of COVID-19 on mental health in China. Psychological Medicine 2021:1 View
  51. Goering S, Klein E, Specker Sullivan L, Wexler A, Agüera y Arcas B, Bi G, Carmena J, Fins J, Friesen P, Gallant J, Huggins J, Kellmeyer P, Marblestone A, Mitchell C, Parens E, Pham M, Rubel A, Sadato N, Teicher M, Wasserman D, Whittaker M, Wolpaw J, Yuste R. Recommendations for Responsible Development and Application of Neurotechnologies. Neuroethics 2021;14(3):365 View
  52. Le Glaz A, Haralambous Y, Kim-Dufor D, Lenca P, Billot R, Ryan T, Marsh J, DeVylder J, Walter M, Berrouiguet S, Lemey C. Machine Learning and Natural Language Processing in Mental Health: Systematic Review. Journal of Medical Internet Research 2021;23(5):e15708 View
  53. Li L, Novillo-Ortiz D, Azzopardi-Muscat N, Kostkova P. Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews. Frontiers in Public Health 2021;9 View
  54. S S, S. Raj J. Analysis of Deep Learning Techniques for Early Detection of Depression on Social Media Network - A Comparative Study. Journal of Trends in Computer Science and Smart Technology 2021;3(1):24 View
  55. Dheeraj K, Ramakrishnudu T. Negative emotions detection on online mental-health related patients texts using the deep learning with MHA-BCNN model. Expert Systems with Applications 2021;182:115265 View
  56. Moreno M, Gaus Q, Wilt M, Arseniev-Koehler A, Ton A, Adrian M, VanderStoep A. Displayed Depression Symptoms on Facebook at Two Time Points: Content Analysis. JMIR Formative Research 2021;5(5):e20179 View
  57. Grzenda A, Kraguljac N, McDonald W, Nemeroff C, Torous J, Alpert J, Rodriguez C, Widge A. Evaluating the Machine Learning Literature: A Primer and User’s Guide for Psychiatrists. American Journal of Psychiatry 2021;178(8):715 View
  58. 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 View
  59. Wongkoblap A, Vadillo M, Curcin V. Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study. JMIR Mental Health 2021;8(8):e19824 View
  60. Wiegersma S, Hidajat M, Schrieken B, Veldkamp B, Olff M. Improving Web-Based Treatment Intake for Multiple Mental and Substance Use Disorders by Text Mining and Machine Learning: Algorithm Development and Validation. JMIR Mental Health 2022;9(4):e21111 View
  61. Amusa L, Twinomurinzi H, Okonkwo C. Modeling COVID-19 incidence with Google Trends. Frontiers in Research Metrics and Analytics 2022;7 View
  62. Hänsel K, Lin I, Sobolev M, Muscat W, Yum-Chan S, De Choudhury M, Kane J, Birnbaum M. Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders. Frontiers in Psychiatry 2021;12 View
  63. Bhattacharya M, Roy S, Chattopadhyay S, Das A, Shetty S. A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges. SECURITY AND PRIVACY 2023;6(1) View
  64. Schindler M, Domahidi E. The computational turn in online mental health research: A systematic review. New Media & Society 2023;25(10):2781 View
  65. Skaik R, Inkpen D. Predicting Depression in Canada by Automatic Filling of Beck’s Depression Inventory Questionnaire. IEEE Access 2022;10:102033 View
  66. Erturk S, Hudson G, Jansli S, Morris D, Odoi C, Wilson E, Clayton-Turner A, Bray V, Yourston G, Cornwall A, Cummins N, Wykes T, Jilka S. Codeveloping and Evaluating a Campaign to Reduce Dementia Misconceptions on Twitter: Machine Learning Study. JMIR Infodemiology 2022;2(2):e36871 View
  67. Chatterjee M, Kumar P, Samanta P, Sarkar D. Suicide ideation detection from online social media: A multi-modal feature based technique. International Journal of Information Management Data Insights 2022;2(2):100103 View
  68. Malhotra A, Jindal R. Deep learning techniques for suicide and depression detection from online social media: A scoping review. Applied Soft Computing 2022;130:109713 View
  69. Jang J, Yoon S, Son G, Kang M, Choeh J, Choi K. Predicting Personality and Psychological Distress Using Natural Language Processing: A Study Protocol. Frontiers in Psychology 2022;13 View
  70. Baniata M, Asghar S. Measuring the impact of social drive across social media forums: a case study of COVID-19. Multimedia Tools and Applications 2022;81(8):10777 View
  71. Wang K, Lin K, Yang S, Na S. The Relationship Between Social Media Digitalization and Coronavirus Disease 2019 Fear Among Service Sector Employees. Frontiers in Psychology 2021;12 View
  72. Xu X. Detecting Suicide Ideation in the Online Environment: A Survey of Methods and Challenges. IEEE Transactions on Computational Social Systems 2022;9(3):679 View
  73. Gupta M, Ramar D, Vijayan R, Gupta N. Artificial Intelligence Tools for Suicide Prevention in Adolescents and Young Adults. Adolescent Psychiatry 2022;12(1):1 View
  74. Ulep A, Deshpande A, Beukes E, Placette A, Manchaiah V. Social Media Use in Hearing Loss, Tinnitus, and Vestibular Disorders: A Systematic Review. American Journal of Audiology 2022;31(3S):1019 View
  75. Safa R, Bayat P, Moghtader L. Automatic detection of depression symptoms in twitter using multimodal analysis. The Journal of Supercomputing 2022;78(4):4709 View
  76. Zhenhua H, Nan W. Empirical analysis based on the related factors of college students’ mental health problems. Frontiers in Psychology 2022;13 View
  77. Karmegam D, Ramamoorthy T, Mappillairajan B. A Systematic Review of Techniques Employed for Determining Mental Health Using Social Media in Psychological Surveillance During Disasters. Disaster Medicine and Public Health Preparedness 2020;14(2):265 View
  78. Elbarazi I, Saddik B, Grivna M, Aziz F, Elsori D, Stip E, Bendak E. The Impact of the COVID-19 “Infodemic” on Well-Being: A Cross-Sectional Study. Journal of Multidisciplinary Healthcare 2022;Volume 15:289 View
  79. Sarkar D, Kumar P, Samanta P, Dutta S, Chatterjee M. A Two-Level Multi-Modal Analysis for Depression Detection From Online Social Media. International Journal of Software Innovation 2022;10(1):1 View
  80. Acosta J, Chandra A, Yeung D, Nelson C, Qureshi N, Blagg T, Martin L. What Data Should Be Included in a Modern Public Health Data System. Big Data 2022;10(S1):S9 View
  81. Gai Z, Fan C, Shen S, Ge Y, Shi Z, Li S, Zhang Y, Cao Y, Chai J. Using Social Media Data to Explore Urban Land Value and Sentiment Inequality: A Case Study of Xiamen, China. Wireless Communications and Mobile Computing 2022;2022:1 View
  82. Hu M, Conway M. Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia. JMIR Infodemiology 2022;2(2):e36941 View
  83. Taghvaei N, Masoumi B, Keyvanpour M. Analytical framework for mental health feature extraction methods in social networks. Intelligent Decision Technologies 2021;15(3):343 View
  84. Ma J, O’Riordan M, Mazzer K, Batterham P, Bradford S, Kõlves K, Titov N, Klein B, Rickwood D. Consumer Perspectives on the Use of Artificial Intelligence Technology and Automation in Crisis Support Services: Mixed Methods Study. JMIR Human Factors 2022;9(3):e34514 View
  85. Suarez-Lledo V, Mejova Y. Behavior Change Around an Online Health Awareness Campaign: A Causal Impact Study. Frontiers in Public Health 2022;10 View
  86. Borba de Souza V, Campos Nobre J, Becker K. DAC Stacking: A Deep Learning Ensemble to Classify Anxiety, Depression, and Their Comorbidity From Reddit Texts. IEEE Journal of Biomedical and Health Informatics 2022;26(7):3303 View
  87. Walsh J, Dwumfour C, Cave J, Griffiths F. Spontaneously generated online patient experience data - how and why is it being used in health research: an umbrella scoping review. BMC Medical Research Methodology 2022;22(1) View
  88. Amusa L, Twinomurinzi H, Phalane E, Phaswana-Mafuya R. Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda. Interactive Journal of Medical Research 2023;12:e42292 View
  89. FOWLER J, MADAN A, BRUCE C, FRUEH B, KASH B, JONES S, SASANGOHAR F. Improving Psychiatric Care Through Integrated Digital Technologies. Journal of Psychiatric Practice 2021;27(2):92 View
  90. Rutter L, Howard J, Lakhan P, Valdez D, Bollen J, Lorenzo-Luaces L. “I Haven’t Been Diagnosed, but I Should Be”—Insight Into Self-diagnoses of Common Mental Health Disorders: Cross-sectional Study. JMIR Formative Research 2023;7:e39206 View
  91. Hu M, Benson R, Chen A, Zhu S, Conway M. Determining the prevalence of cannabis, tobacco, and vaping device mentions in online communities using natural language processing. Drug and Alcohol Dependence 2021;228:109016 View
  92. Hacohen-Kerner Y, Manor N, Goldmeier M, Bachar E. Detection of Anorexic Girls-In Blog Posts Written in Hebrew Using a Combined Heuristic AI and NLP Method. IEEE Access 2022;10:34800 View
  93. Diniz E, Fontenele J, de Oliveira A, Bastos V, Teixeira S, Rabêlo R, Calçada D, dos Santos R, de Oliveira A, Teles A. Boamente: A Natural Language Processing-Based Digital Phenotyping Tool for Smart Monitoring of Suicidal Ideation. Healthcare 2022;10(4):698 View
  94. Smrke U, Mlakar I, Lin S, Musil B, Plohl N. Language, Speech, and Facial Expression Features for Artificial Intelligence–Based Detection of Cancer Survivors’ Depression: Scoping Meta-Review. JMIR Mental Health 2021;8(12):e30439 View
  95. Rogers D, Preece A, Innes M, Spasic I. Real-Time Text Classification of User-Generated Content on Social Media: Systematic Review. IEEE Transactions on Computational Social Systems 2022;9(4):1154 View
  96. Figuerêdo J, Maia A, Calumby R. Early depression detection in social media based on deep learning and underlying emotions. Online Social Networks and Media 2022;31:100225 View
  97. Iyortsuun N, Kim S, Jhon M, Yang H, Pant S. A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis. Healthcare 2023;11(3):285 View
  98. Metzler H, Baginski H, Niederkrotenthaler T, Garcia D. Detecting Potentially Harmful and Protective Suicide-Related Content on Twitter: Machine Learning Approach. Journal of Medical Internet Research 2022;24(8):e34705 View
  99. Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. International Journal of Medical Informatics 2022;166:104855 View
  100. Li X, Arif M. Research on the Application of Data Mining Technology in College Students’ Mental Health Education in the Network Age. Security and Communication Networks 2022;2022:1 View
  101. Varanasi L, Dasari C. Deep Learning based techniques for Neuro-degenerative disorders detection. Engineering Applications of Artificial Intelligence 2023;122:106103 View
  102. Lin Y, Alshehri Y, Alnazzawi N, Abid M, Khan S, Jabeen F, Elwarfalli I. Social media analytics and their applications to evaluate an activity in online health interventions using CRITIC and TOPSIS techniques. Soft Computing 2023 View
  103. Di Cara N, Maggio V, Davis O, Haworth C. Methodologies for Monitoring Mental Health on Twitter: Systematic Review. Journal of Medical Internet Research 2023;25:e42734 View
  104. Cascalheira C, Flinn R, Zhao Y, Klooster D, Laprade D, Hamdi S, Scheer J, Gonzalez A, Lund E, Gomez I, Saha K, De Choudhury M. Models of Gender Dysphoria Using Social Media Data for Use in Technology-Delivered Interventions: Machine Learning and Natural Language Processing Validation Study. JMIR Formative Research 2023;7:e47256 View
  105. Guiñazú M, González M, Ruiz R, Hernández V, Diez S, Velásquez J. A novel depression risk prediction model based on data fusion from Chilean National Health Surveys to diagnose risk depression among patients with mood disorders. Information Fusion 2023;100:101960 View
  106. Khorasani M, Kahani M, Yazdi S, Hajiaghaei-Keshteli M. Towards finding the lost generation of autistic adults: A deep and multi-view learning approach on social media. Knowledge-Based Systems 2023;276:110724 View
  107. Arji G, Erfannia L, alirezaei S, Hemmat M. A systematic literature review and analysis of deep learning algorithms in mental disorders. Informatics in Medicine Unlocked 2023;40:101284 View
  108. Chatterjee M, Kumar P, Sarkar D. Generating a Mental Health Curve for Monitoring Depression in Real Time by Incorporating Multimodal Feature Analysis Through Social Media Interactions. International Journal of Intelligent Information Technologies 2023;19(1):1 View
  109. Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior K, Poirrier J, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Review of Pharmacoeconomics & Outcomes Research 2024;24(1):63 View
  110. Nghiem J, Adler D, Estrin D, Livesey C, Choudhury T. Understanding Mental Health Clinicians’ Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study. JMIR Formative Research 2023;7:e47380 View
  111. Villa-Pérez M, Trejo L, Moin M, Stroulia E. Extracting Mental Health Indicators From English and Spanish Social Media: A Machine Learning Approach. IEEE Access 2023;11:128135 View
  112. Kerz E, Zanwar S, Qiao Y, Wiechmann D. Toward explainable AI (XAI) for mental health detection based on language behavior. Frontiers in Psychiatry 2023;14 View
  113. Shahzadi I, Fuzail M, Aslam D. Deep Emotions Recognition from Facial Expressions using Deep Learning. VFAST Transactions on Software Engineering 2023;11(2):58 View
  114. Jin Z, Su R, Liu Y, Duan C. A psychological evaluation method incorporating noisy label correction mechanism. Soft Computing 2024 View
  115. Mieles Toloza I, Delgado Meza J, Acevedo-Suárez J. Análisis del Lenguaje Natural para la Identificación de Alteraciones Mentales en Redes Sociales: Una Revisión Sistemática de Estudios. Revista Politécnica 2024;53(1):57 View
  116. Nicomedes C, Sasot C, Santos G, Distor J, Marzan P, Manda A. A Convergent-mixed Method Study on the Attitudes and Perception Towards Suicide Memes and Suicidality. The Open Psychology Journal 2024;17(1) View
  117. Bayram H, Öztürkcan A. Türkiye’de Popüler Diyet Terimlerine Gösterilen İlginin İncelenmesi: Bir İnfodemiyoloji Çalışması. Bandırma Onyedi Eylül Üniversitesi Sağlık Bilimleri ve Araştırmaları Dergisi 2024;6(1):120 View

Books/Policy Documents

  1. de Araújo Novaes M, Basu A. Fundamentals of Telemedicine and Telehealth. View
  2. Baba T, Baba K, Ikeda D. Advanced Information Networking and Applications. View
  3. Teles A, Barros F, Rodrigues I, Barbosa A, Silva F, Coutinho L, Teixeira S. IoT and ICT for Healthcare Applications. View
  4. . Fundamentals of Telemedicine and Telehealth. View
  5. Valdez R, Keim-Malpass J. Social Web and Health Research. View
  6. Ebert D, Harrer M, Apolinário-Hagen J, Baumeister H. Frontiers in Psychiatry. View
  7. Jain P, Quadri S. Intelligent Data Communication Technologies and Internet of Things. View
  8. Bhunia G, Shit P. Spatial Modeling in Forest Resources Management. View
  9. Arora S, Malik A, Khurana P, Batra I. Advanced Informatics for Computing Research. View
  10. Gupta S, Mehndiratta N, Sinha S, Chaturvedi S, Singla M. Biomedical Data Mining for Information Retrieval. View
  11. Tenenbaum E, Ranallo P, Hastings J. Mental Health Informatics. View
  12. Hussain Z, Borah M. Deep Learning for Social Media Data Analytics. View
  13. Andavar V, Gupta S. Bio-Inspired Algorithms and Devices for Treatment of Cognitive Diseases Using Future Technologies. View
  14. Kern M, Armstrong P. Encyclopedia of Mental Health. View
  15. Wajnerman A, López-Silva P. Protecting the Mind. View
  16. Ingram W, Khanna R, Weston C. Mental Health Informatics. View
  17. Cachia A, Camilleri V, Dingli A, Galea M, Grech P, Sammut A, Scerri J. Ethical Implications of Reshaping Healthcare With Emerging Technologies. View
  18. Hilty D, Armstrong C, Edwards-Stewart A, Luxton D. Digital Therapeutics for Mental Health and Addiction. View
  19. Smirnov I, Stankevich M, Kuznetsova Y, Suvorova M, Larionov D, Nikitina E, Savelov M, Grigoriev O. Artificial Intelligence. View
  20. Mubashir A, Shafique N, Bibi E. Panic Buying and Environmental Disasters. View
  21. Arora S, Malik A. Computational Intelligence for Engineering and Management Applications. View
  22. Chatterjee M, Modak S, Sarkar D. Cognitive Cardiac Rehabilitation Using IoT and AI Tools. View
  23. Safa R, Edalatpanah S, Sorourkhah A. Deep Learning in Personalized Healthcare and Decision Support. View
  24. Tapu M, Akash R, Al Fahim H, Jarin T, Bhuiyan T, Reza A, Arefin M. Intelligent Computing and Optimization. View