(Publisher of Peer Reviewed Open Access Journals)
Navigation

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-8 Issue-83 October-2021
Full-Text PDF
Paper Title : A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection
Author Name : Ravita Chahar, Ashutosh Kumar Dubey and Sushil Kumar Narang
Abstract :

Cases of mental health issues are increasing continuously and have sped up due to COVID-19. There are high chances of developing mental health issues such as depression, anxiety, schizophrenia, and dementia after 2–3 months of COVID-19 diagnosis. In this paper, a review and meta-analysis of machine intelligence approaches—namely, machine learning, deep learning (deep learning with hybrid boosting), and machine vision methods—for mental health issues and depression detection were presented. Meta-analysis was performed in four parts. The first part focused on the publication trends, criteria for inclusion and exclusion, and the current methodological scenario. The second part was intended for the methods and their advantages and limitations. It covered mental health issues and depression detection techniques along with the challenges. The third part focused on the discussion and applicability of datasets. The fourth part focused on the complete analysis and discussion along with suggestive measures; moreover, it covered the overall analysis, including the methodological impact, result impact, current trends, and some suggestions based on the limitations and challenges.

Keywords : Machine intelligence, Machine learning, Deep learning, Mental health issues, Depression detection.
Cite this article : Chahar R, Dubey AK, Narang SK. A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection . International Journal of Advanced Technology and Engineering Exploration. 2021; 8(83):1279-1314. DOI:10.19101/IJATEE.2021.874198.
References :
[1]Rajkumar RP. COVID-19 and mental health: a review of the existing literature. Asian Journal of Psychiatry. 2020.
[Crossref] [Google Scholar]
[2]Salari N, Hosseinian-far A, Jalali R, Vaisi-raygani A, Rasoulpoor S, Mohammadi M, et al. Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. Globalization and Health. 2020; 16(1):1-11.
[Crossref] [Google Scholar]
[3]Ustun G. Determining depression and related factors in a society affected by COVID-19 pandemic. The International Journal of Social Psychiatry. 2021; 67(1):54-63.
[Crossref] [Google Scholar]
[4]Ghazali N, Zain NH, Fesol SF, Moketar NA, Odzaly EE, Teo NH. Relationship between learning habits and socioeconomic status: a COVID-19 pandemic study. International Journal of Advanced Technology and Engineering Exploration. 2021; 8(74):102-13.
[Crossref] [Google Scholar]
[5]World Health Organization. Depression and other common mental disorders: global health estimates. World Health Organization; 2017.
[Google Scholar]
[6]Abbott A. COVIDs mental-health toll: how scientists are tracking a surge in depression. Nature. 2021; 590:194-5.
[Crossref] [Google Scholar]
[7]Office for National Statistics (UK data); Centers for Disease Control and Prevention (US data), 2020.
[8]Satinsky EN, Kimura T, Kiang MV, Abebe R, Cunningham S, Lee H, et al. Systematic review and meta-analysis of depression, anxiety, and suicidal ideation among Ph.D. students. Scientific Reports. 2021; 11(1):1-12.
[Crossref] [Google Scholar]
[9]Charles ST, Karnaze MM, Leslie FM. Positive factors related to graduate student mental health. Journal of American College Health. 2021:1-9.
[Crossref] [Google Scholar]
[10]Ibrahim EN, Jamali N, Suhaimi AI. Exploring gamification design elements for mental health support. International Journal of Advanced Technology and Engineering Exploration. 2021; 8(74):114-25.
[Crossref] [Google Scholar]
[11]Cook BL, Progovac AM, Chen P, Mullin B, Hou S, Baca-Garcia E. Novel use of natural language processing (NLP) to predict suicidal ideation and psychiatric symptoms in a text-based mental health intervention in Madrid. Computational and Mathematical Methods in Medicine. 2016.
[Google Scholar]
[12]Rambocas M, Gama J. Marketing research: the role of sentiment analysis. University of Porto, Faculty of Economics of Porto; 2013.
[Google Scholar]
[13]Baden M, Lu L, Drummond FJ, Gavin A, Sharp L. Pain, fatigue and depression symptom cluster in survivors of prostate cancer. Supportive Care in Cancer. 2020; 28(10):4813-24.
[Crossref] [Google Scholar]
[14]Alhassan AA, Alqadhib EM, Taha NW, Alahmari RA, Salam M, Almutairi AF. The relationship between addiction to smartphone usage and depression among adults: a cross sectional study. BMC Psychiatry. 2018; 18:1-8.
[Crossref] [Google Scholar]
[15]Stanković M, Nešić M, Čičević S, Shi Z. Association of smartphone use with depression, anxiety, stress, sleep quality, and internet addiction. Empirical evidence from a smartphone application. Personality and Individual Differences. 2021.
[Crossref] [Google Scholar]
[16]Matar BJ, Jaalouk D. Depression, anxiety, and smartphone addiction in university students-a cross sectional study. PloS ONE. 2017; 12(8).
[Crossref] [Google Scholar]
[17]Havigerová JM, Haviger J, Kučera D, Hoffmannová P. Text-based detection of the risk of depression. Frontiers in Psychology. 2019.
[Crossref] [Google Scholar]
[18]Chahar R, Kaur D. A systematic review of the machine learning algorithms for the computational analysis in different domains. International Journal of Advanced Technology and Engineering Exploration. 2020; 7(71):147-64.
[Crossref] [Google Scholar]
[19]Dubey AK, Narang S, Kumar A, Satya MS, Garcia-diaz V. Performance estimation of machine learning algorithms in the factor analysis of COVID-19 dataset. Computers, Materials, & Continua. 2021; 66(2):1921-36.
[Google Scholar]
[20]Zhao M, Feng Z. Machine learning methods to evaluate the depression status of Chinese recruits: a diagnostic study. Neuropsychiatric Disease and Treatment. 2020:2743-52.
[Crossref] [Google Scholar]
[21]Kwakernaak S, Van MK, Cahn W, Janssen R, Investigators G. Using machine learning to predict mental healthcare consumption in non-affective psychosis. Schizophrenia Research. 2020; 218:166-72.
[Crossref] [Google Scholar]
[22]Kim J, Lee D, Park E. Machine learning for mental health in social media: bibliometric study. Journal of Medical Internet Research. 2021; 23(3):1-17.
[Crossref] [Google Scholar]
[23]Gao S, Calhoun VD, Sui J. Machine learning in major depression: from classification to treatment outcome prediction. CNS Neuroscience & Therapeutics. 2018; 24(11):1037-52.
[Crossref] [Google Scholar]
[24]Shatte AB, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychological Medicine. 2019; 49(9):1426-48.
[Crossref] [Google Scholar]
[25]Chen L, Xia C, Sun H. Recent advances of deep learning in psychiatric disorders. Precision Clinical Medicine. 2020; 3(3):202-13.
[Crossref] [Google Scholar]
[26]Govindasamy KA, Palanichamy N. Depression detection using machine learning techniques on twitter data. In international conference on intelligent computing and control systems 2021 (pp. 960-6). IEEE.
[Crossref] [Google Scholar]
[27]Cacheda F, Fernandez D, Novoa FJ, Carneiro V. Early detection of depression: social network analysis and random forest techniques. Journal of Medical Internet Research. 2019; 21(6).
[Crossref] [Google Scholar]
[28]Zhu H, Han G, Shu L, Zhao H. Arvanet: deep recurrent architecture for PPG-based negative mental-state monitoring. IEEE Transactions on Computational Social Systems. 2020; 8(1):179-90.
[Crossref] [Google Scholar]
[29]Laden D, Jayasundara S, Kahanda I. RDoCer: information retrieval and sentence extraction for mental health using research domain criteria. In international conference on semantic computing 2020 (pp. 226-9). IEEE.
[Crossref] [Google Scholar]
[30]Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Journal of Clinical Epidemiology. 2009; 62(10):e1-34.
[Crossref] [Google Scholar]
[31]Dai W, Han D, Dai Y, Xu D. Emotion recognition and affective computing on vocal social media. Information & Management. 2015; 52(7):777-88.
[Crossref] [Google Scholar]
[32]Marín-morales J, Higuera-trujillo JL, Greco A, Guixeres J, Llinares C, Scilingo EP, et al. Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors. Scientific Reports. 2018; 8(1):1-15.
[Crossref] [Google Scholar]
[33]Perveen N, Ahmad N, Khan MA, Khalid R, Qadri S. Facial expression recognition through machine learning. International Journal of Scientific & Technology Research. 2016; 5(03):91-7.
[Google Scholar]
[34]Ibrahim BR, Khalifa FM, Zeebaree SR, Othman NA, Alkhayyat A, Zebari RR, et al. Embedded system for eye blink detection using machine learning technique. In Babylon international conference on information technology and science 2021 (pp. 58-62). IEEE.
[Crossref] [Google Scholar]
[35]Ray A, Kumar S, Reddy R, Mukherjee P, Garg R. Multi-level attention network using text, audio and video for depression prediction. In proceedings of the international on audio/visual emotion challenge and workshop 2019 (pp. 81-8).
[Crossref] [Google Scholar]
[36]Thorstad R, Wolff P. Predicting future mental illness from social media: a big-data approach. Behavior Research Methods. 2019; 51(4):1586-600.
[Crossref] [Google Scholar]
[37]Rajeswari A, Sowmbika P, Kalaimagal P, Ramya M, Ranjitha M. Improved emotional speech recognition algorithms. In international conference on wireless communications, signal processing and networking 2016 (pp. 2362-6). IEEE.
[Google Scholar]
[38]Neitzke AB. An illness of power: gender and the social causes of depression. Culture, Medicine, and Psychiatry. 2016; 40(1):59-73.
[Crossref] [Google Scholar]
[39]Jung Y, Yoon YI. Multi-level assessment model for wellness service based on human mental stress level. Multimedia Tools and Applications. 2017; 76(9):11305-17.
[Crossref] [Google Scholar]
[40]Farhan AA, Yue C, Morillo R, Ware S, Lu J, Bi J, et al. Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data. In wireless health 2016 (pp. 1-8). IEEE.
[Crossref] [Google Scholar]
[41]Demirci K, Akgönül M, Akpinar A. Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. Journal of Behavioral Addictions. 2015; 4(2):85-92.
[Crossref] [Google Scholar]
[42]Shim M, Hwang HJ, Kim DW, Lee SH, Im CH. Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features. Schizophrenia Research. 2016; 176(2-3):314-9.
[Crossref] [Google Scholar]
[43]Lins AJ, Muniz MT, Garcia AN, Gomes AV, Cabral RM, Bastos-filho CJ. Using artificial neural networks to select the parameters for the prognostic of mild cognitive impairment and dementia in elderly individuals. Computer Methods and Programs in Biomedicine. 2017; 152:93-104.
[Crossref] [Google Scholar]
[44]Al JM, Guo G. Video-based depression level analysis by encoding deep spatiotemporal features. IEEE Transactions on Affective Computing. 2018.
[Google Scholar]
[45]Iliou T, Konstantopoulou G, Ntekouli M, Lymperopoulou C, Assimakopoulos K, Galiatsatos D, et al. ILIOU machine learning preprocessing method for depression type prediction. Evolving Systems. 2019; 10(1):29-39.
[Crossref] [Google Scholar]
[46]Tariq S, Akhtar N, Afzal H, Khalid S, Mufti MR, Hussain S, et al. A novel co-training-based approach for the classification of mental illnesses using social media posts. IEEE Access. 2019; 7:166165-72.
[Google Scholar]
[47]Ay B, Yildirim O, Talo M, Baloglu UB, Aydin G, Puthankattil SD, et al. Automated depression detection using deep representation and sequence learning with EEG signals. Journal of Medical Systems. 2019; 43(7).
[Crossref] [Google Scholar]
[48]Li X, Zhang X, Zhu J, Mao W, Sun S, Wang Z, et al. Depression recognition using machine learning methods with different feature generation strategies. Artificial Intelligence in Medicine. 2019.
[Crossref] [Google Scholar]
[49]Besteher B, Gaser C, Nenadić I. Machine-learning based brain age estimation in major depression showing no evidence of accelerated aging. Psychiatry Research: Neuroimaging. 2019; 290:1-4.
[Crossref] [Google Scholar]
[50]Xing Y, Rao N, Miao M, Li Q, Li Q, Chen X, et al. Task-state heart rate variability parameter-based depression detection model and effect of therapy on the parameters. IEEE Access. 2019; 7:105701-9.
[Crossref] [Google Scholar]
[51]Yang X, Mcewen R, Ong LR, Zihayat M. A big data analytics framework for detecting user-level depression from social networks. International Journal of Information Management. 2020; 54:102141.
[Crossref] [Google Scholar]
[52]Altameem T, Amoon M, Altameem A. A deep reinforcement learning process based on robotic training to assist mental health patients. Neural Computing and Applications. 2020:1-10.
[Crossref] [Google Scholar]
[53]Abd RR, Omar K, Noah SA, Danuri MS, Al-garadi MA. Application of machine learning methods in mental health detection: a systematic review. IEEE Access. 2020; 8:183952-64.
[Crossref] [Google Scholar]
[54]Kumar P, Garg S, Garg A. Assessment of anxiety, depression and stress using machine learning models. Procedia Computer Science. 2020; 171:1989-98.
[Crossref] [Google Scholar]
[55]Yue C, Ware S, Morillo R, Lu J, Shang C, Bi J, et al. Automatic depression prediction using internet traffic characteristics on smartphones. Smart Health. 2020; 18:100137.
[Crossref] [Google Scholar]
[56]Baek JW, Chung K. Context deep neural network model for predicting depression risk using multiple regression. IEEE Access. 2020; 8:18171-81.
[Crossref] [Google Scholar]
[57]Cai H, Qu Z, Li Z, Zhang Y, Hu X, Hu B. Feature-level fusion approaches based on multimodal EEG data for depression recognition. Information Fusion. 2020; 59:127-38.
[Crossref] [Google Scholar]
[58]Qin FY, Lv ZQ, Wang DN, Hu B, Wu C. Health status prediction for the elderly based on machine learning. Archives of Gerontology and Geriatrics. 2020.
[Crossref] [Google Scholar]
[59]Sharma A, Verbeke WJ. Improving diagnosis of depression with XGBOOST machine learning model and a large biomarkers Dutch dataset (n= 11,081). Frontiers in Big Data. 2020.
[Crossref] [Google Scholar]
[60]Morrow AS, Campos Vega AD, Zhao X, Liriano MM. Leveraging machine learning to identify predictors of receiving psychosocial treatment for attention deficit/hyperactivity disorder. Administration and Policy in Mental Health and Mental Health Services Research. 2020; 47:680-92.
[Google Scholar]
[61]Lin GM, Nagamine M, Yang SN, Tai YM, Lin C, Sato H. Machine learning based suicide ideation prediction for military personnel. IEEE Journal of Biomedical and Health Informatics. 2020; 24(7):1907-16.
[Crossref] [Google Scholar]
[62]Su C, Aseltine R, Doshi R, Chen K, Rogers SC, Wang F. Machine learning for suicide risk prediction in children and adolescents with electronic health records. Translational Psychiatry. 2020; 10(1):1-10.
[Crossref] [Google Scholar]
[63]Priya A, Garg S, Tigga NP. Predicting anxiety, depression and stress in modern life using machine learning algorithms. Procedia Computer Science. 2020; 167:1258-67.
[Crossref] [Google Scholar]
[64]Frässle S, Marquand AF, Schmaal L, Dinga R, Veltman DJ, Van DWNJ, et al. Predicting individual clinical trajectories of depression with generative embedding. NeuroImage: Clinical. 2020.
[Crossref] [Google Scholar]
[65]Tate AE, Mccabe RC, Larsson H, Lundström S, Lichtenstein P, Kuja-halkola R. Predicting mental health problems in adolescence using machine learning techniques. PloS ONE. 2020; 15(4).
[Crossref] [Google Scholar]
[66]Betts KS, Kisely S, Alati R. Predicting postpartum psychiatric admission using a machine learning approach. Journal of Psychiatric Research. 2020; 130:35-40.
[Crossref] [Google Scholar]
[67]Lin A, Stolfi A, Eicher T, Neeley S. Predicting second-generation antidepressant effectiveness in treating sadness using demographic and clinical information: a machine learning approach. Journal of Affective Disorders. 2020; 272:295-304.
[Crossref] [Google Scholar]
[68]Cho SE, Geem ZW, Na KS. Prediction of depression among medical check-ups of 433,190 patients: a nationwide population-based study. Psychiatry Research. 2020.
[Crossref] [Google Scholar]
[69]Gonçalves C, Ferreira D, Neto C, Abelha A, Machado J. Prediction of mental illness associated with unemployment using data mining. Procedia Computer Science. 2020; 177:556-61.
[Crossref] [Google Scholar]
[70]Martínez-Monteagudo MC, Delgado B, Díaz-Herrero Á, García-Fernández JM. Relationship between suicidal thinking, anxiety, depression and stress in university students who are victims of cyberbullying. Psychiatry Research. 2020; 286:112856.
[Crossref] [Google Scholar]
[71]Sağbaş EA, Korukoglu S, Balli S. Stress detection via keyboard typing behaviors by using smartphone sensors and machine learning techniques. Journal of Medical Systems. 2020; 44(4):1-12.
[Crossref] [Google Scholar]
[72]Castillo-Sánchez G, Marques G, Dorronzoro E, Rivera-Romero O, Franco-Martín M, De la Torre-Díez I. Suicide risk assessment using machine learning and social networks: a scoping review. Journal of Medical Systems. 2020; 44(12):1-5.
[Crossref] [Google Scholar]
[73]Guntuku SC, Sherman G, Stokes DC, Agarwal AK, Seltzer E, Merchant RM, et al. Tracking mental health and symptom mentions on twitter during covid-19. Journal of General Internal Medicine. 2020; 35(9):2798-800.
[Crossref] [Google Scholar]
[74]Elhai JD, Yang H, Rozgonjuk D, Montag C. Using machine learning to model problematic smartphone use severity: the significant role of fear of missing out. Addictive Behaviors. 2020.
[Crossref] [Google Scholar]
[75]Oyebode O, Alqahtani F, Orji R. Using machine learning and thematic analysis methods to evaluate mental health apps based on user reviews. IEEE Access. 2020; 8:111141-58.
[Crossref] [Google Scholar]
[76]Zhang Y, Wang S, Hermann A, Joly R, Pathak J. Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women. Journal of Affective Disorders. 2021; 279:1-8.
[Crossref] [Google Scholar]
[77]Hong S, Liu YS, Cao B, Cao J, Ai M, Chen J, et al. Identification of suicidality in adolescent major depressive disorder patients using sMRI: a machine learning approach. Journal of Affective Disorders. 2021; 280:72-6.
[Crossref] [Google Scholar]
[78]Zulfiker MS, Kabir N, Biswas AA, Nazneen T, Uddin MS. An in-depth analysis of machine learning approaches to predict depression. Current Research in Behavioral Sciences. 2021.
[Crossref] [Google Scholar]
[79]Parghi N, Chennapragada L, Barzilay S, Newkirk S, Ahmedani B, Lok B, et al. Assessing the predictive ability of the suicide crisis inventory for near‐term suicidal behavior using machine learning approaches. International Journal of Methods in Psychiatric Research. 2021; 30(1).
[Crossref] [Google Scholar]
[80]He L, Chan JC, Wang Z. Automatic depression recognition using CNN with attention mechanism from videos. Neurocomputing. 2021; 422:165-75.
[Crossref] [Google Scholar]
[81]Wardenaar KJ, Riese H, Giltay EJ, Eikelenboom M, Van HAJ, Beekman AF, et al. Common and specific determinants of 9-year depression and anxiety course-trajectories: a machine-learning investigation in the Netherlands Study of Depression and Anxiety (NESDA). Journal of Affective Disorders. 2021; 293:295-304.
[Crossref] [Google Scholar]
[82]Uddin MZ, Dysthe KK, Følstad A, Brandtzaeg PB. Deep learning for prediction of depressive symptoms in a large textual dataset. Neural Computing and Applications. 2021: 1-24.
[Crossref] [Google Scholar]
[83]Wang R, Hao Y, Yu Q, Chen M, Humar I, Fortino G. Depression analysis and recognition based on functional near-infrared spectroscopy. IEEE Journal of Biomedical and Health Informatics. 2021.
[Google Scholar]
[84]Shin D, Cho WI, Park C, Rhee SJ, Kim MJ, Lee H, et al. Detection of minor and major depression through voice as a biomarker using machine learning. Journal of Clinical Medicine. 2021; 10(14):1-15.
[Crossref] [Google Scholar]
[85]Shi D, Li Y, Zhang H, Yao X, Wang S, Wang G, et al. Machine learning of schizophrenia detection with structural and functional neuroimaging. Disease Markers. 2021.
[Crossref] [Google Scholar]
[86]Richter T, Fishbain B, Fruchter E, Richter-levin G, Okon-singer H. Machine learning-based diagnosis support system for differentiating between clinical anxiety and depression disorders. Journal of Psychiatric Research. 2021; 141:199-205.
[Crossref] [Google Scholar]
[87]Na KS, Cho SE, Cho SJ. Machine learning-based discrimination of panic disorder from other anxiety disorders. Journal of Affective Disorders. 2021; 278:1-4.
[Crossref] [Google Scholar]
[88]Aljameel SS, Khan IU, Aslam N, Aljabri M, Alsulmi ES. Machine learning-based model to predict the disease severity and outcome in COVID-19 patients. Scientific Programming. 2021.
[Crossref] [Google Scholar]
[89]Cho SE, Geem ZW, Na KS. Predicting depression in community dwellers using a machine learning algorithm. Diagnostics. 2021; 11(8):1-12.
[Crossref] [Google Scholar]
[90]Gokten ES, Uyulan C. Prediction of the development of depression and post-traumatic stress disorder in sexually abused children using a random forest classifier. Journal of Affective Disorders. 2021; 279:256-65.
[Crossref] [Google Scholar]
[91]Huberts LC, Does RJ, Ravesteijn B, Lokkerbol J. Predictive monitoring using machine learning algorithms and a real‐life example on schizophrenia. Quality and Reliability Engineering International. 2021.
[Crossref] [Google Scholar]
[92]Du YL, Hu JB, Huang TT, Lai JB, Ng CH, Zhang WH, et al. Psychometric properties of the clinically useful depression outcome scale supplemented with DSM-5 mixed subtype questionnaire in Chinese patients with mood disorders. Journal of Affective Disorders. 2021; 279:53-8.
[Crossref] [Google Scholar]
[93]Liu Y, Hankey J, Cao B, Chokka P. Screening for major depressive disorder in a tertiary mental health centre using earlydetect: a machine learning-based pilot study. Journal of Affective Disorders Reports. 2021; 3:1-6.
[Crossref] [Google Scholar]
[94]Li W, Wang Q, Liu X, Yu Y. Simple action for depression detection: using kinect-recorded human kinematic skeletal data. BMC Psychiatry. 2021; 21(1):1-11.
[Crossref] [Google Scholar]
[95]Salankar N, Koundal D, Mian QS. Stress classification by multimodal physiological signals using variational mode decomposition and machine learning. Journal of Healthcare Engineering. 2021.
[Crossref] [Google Scholar]
[96]Liu Z, Hu B, Yan L, Wang T, Liu F, Li X, et al. Detection of depression in speech. In international conference on affective computing and intelligent interaction 2015 (pp. 743-7). IEEE.
[Crossref] [Google Scholar]
[97]Rukavina S, Gruss S, Hoffmann H, Tan JW, Walter S, Traue HC. Affective computing and the impact of gender and age. PloS One. 2016; 11(3):1-20.
[Crossref] [Google Scholar]
[98]Cai J, Wang ZJ, Appel-cresswell S, Mckeown MJ. Feature selection to simplify BDI for efficient depression identification. In Canadian conference on electrical and computer engineering 2016 (pp. 1-4). IEEE.
[Crossref] [Google Scholar]
[99]Li X, Cao T, Sun S, Hu B, Ratcliffe M. Classification study on eye movement data: Towards a new approach in depression detection. In congress on evolutionary computation 2016 (pp. 1227-32). IEEE.
[Crossref] [Google Scholar]
[100]Alghowinem S, Goecke R, Wagner M, Epps J, Hyett M, Parker G, Breakspear M. Multimodal depression detection: fusion analysis of paralinguistic, head pose and eye gaze behaviors. IEEE Transactions on Affective Computing. 2016; 9(4):478-90.
[Crossref] [Google Scholar]
[101] Deshpande M, Rao V. Depression detection using emotion artificial intelligence. In international conference on intelligent sustainable systems 2017 (pp. 858-62). IEEE.
[Crossref] [Google Scholar]
[102]Jan A, Meng H, Gaus YF, Zhang F. Artificial intelligent system for automatic depression level analysis through visual and vocal expressions. IEEE Transactions on Cognitive and Developmental Systems. 2017; 10(3):668-80.
[Crossref] [Google Scholar]
[103]Zhu Y, Shang Y, Shao Z, Guo G. Automated depression diagnosis based on deep networks to encode facial appearance and dynamics. IEEE Transactions on Affective Computing. 2017; 9(4):578-84.
[Crossref] [Google Scholar]
[104]Pampouchidou A, Simos PG, Marias K, Meriaudeau F, Yang F, Pediaditis M, et al. Automatic assessment of depression based on visual cues: a systematic review. IEEE Transactions on Affective Computing. 2017; 10(4):445-70.
[Crossref] [Google Scholar]
[105]Kiss G, Vicsi K. Comparison of read and spontaneous speech in case of automatic detection of depression. In international conference on cognitive infocommunications 2017 (pp. 213-8). IEEE.
[Crossref] [Google Scholar]
[106]Dibeklioğlu H, Hammal Z, Cohn JF. Dynamic multimodal measurement of depression severity using deep autoencoding. IEEE Journal of Biomedical and Health Informatics. 2017; 22(2):525-36.
[Crossref] [Google Scholar]
[107]Zucco C, Calabrese B, Cannataro M. Sentiment analysis and affective computing for depression monitoring. In international conference on bioinformatics and biomedicine 2017 (pp. 1988-95). IEEE.
[Crossref] [Google Scholar]
[108]Madhav KC, Sherchand SP, Sherchan S. Association between screen time and depression among US adults. Preventive Medicine Reports. 2017; 8:67-71.
[Crossref] [Google Scholar]
[109]Pampouchidou A, Pediaditis M, Maridaki A, Awais M, Vazakopoulou CM, Sfakianakis S, et al. Quantitative comparison of motion history image variants for video-based depression assessment. EURASIP Journal on Image and Video Processing. 2017:1-11.
[Crossref] [Google Scholar]
[110]Ingle VK, Pandey I, Singh AR, Pakhare A, Kumar S. Screening of patients with chronic medical disorders in the outpatient department for depression using handheld computers as interface and patient health questionnaire-9 as a tool. International Journal of Applied and Basic Medical Research. 2017; 7(2):129-33.
[Crossref] [Google Scholar]
[111]Kulkarni PB, Patil MM. Clinical depression detection in adolescent by face. In international conference on smart city and emerging technology 2018 (pp. 1-4). IEEE.
[Crossref] [Google Scholar]
[112]Al-gawwam S, Benaissa M. Depression detection from eye blink features. In international symposium on signal processing and information technology 2018 (pp. 388-92). IEEE.
[Crossref] [Google Scholar]
[113]Stepanov EA, Lathuiliere S, Chowdhury SA, Ghosh A, Vieriu RL, Sebe N, et al. Depression severity estimation from multiple modalities. In international conference on e-health networking, applications and services 2018 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[114]He L, Jiang D, Sahli H. Automatic depression analysis using dynamic facial appearance descriptor and dirichlet process fisher encoding. IEEE Transactions on Multimedia. 2018; 21(6):1476-86.
[Crossref] [Google Scholar]
[115]Kacem A, Hammal Z, Daoudi M, Cohn J. Detecting depression severity by interpretable representations of motion dynamics. In international conference on automatic face & gesture recognition 2018 (pp. 739-45). IEEE.
[Crossref] [Google Scholar]
[116]Islam MR, Kamal AR, Sultana N, Islam R, Moni MA. Detecting depression using k-nearest neighbors (knn) classification technique. In international conference on computer, communication, chemical, material and electronic engineering 2018 (pp. 1-4). IEEE.
[Crossref] [Google Scholar]
[117]Kiss G, Takács AB, Sztahó D, Vicsi K. Detection possibilities of depression and parkinson’s disease based on the ratio of transient parts of the speech. In international conference on cognitive infocommunications 2018 (pp.165-8). IEEE.
[Crossref] [Google Scholar]
[118]Katchapakirin K, Wongpatikaseree K, Yomaboot P, Kaewpitakkun Y. Facebook social media for depression detection in the Thai community. In international joint conference on computer science and software engineering 2018 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[119]Nakamura M, Shinohara S, Omiya Y, Higuchi M, Mitsuyoshi S, Takano T, et al. Feasibility study for estimation of depression severity using voice analysis. In international conference on bioinformatics and biomedicine 2018 (pp. 2792-4). IEEE.
[Crossref] [Google Scholar]
[120]Nakamura R, Mitsukura Y. Feature analysis of electroencephalography in patients with depression. In life sciences conference 2018 (pp. 53-6). IEEE.
[Crossref] [Google Scholar]
[121]Song S, Shen L, Valstar M. Human behaviour-based automatic depression analysis using hand-crafted statistics and deep learned spectral features. In international conference on automatic face & gesture recognition 2018 (pp. 158-65). IEEE.
[Crossref] [Google Scholar]
[122]Li J, Fu X, Shao Z, Shang Y. Improvement on speech depression recognition based on deep networks. In Chinese automation congress 2018 (pp. 2705-9). IEEE.
[Crossref] [Google Scholar]
[123]Yang L, Jiang D, Sahli H. Integrating deep and shallow models for multi-modal depression analysis—hybrid architectures. IEEE Transactions on Affective Computing. 2018; 12(1):239-53.
[Crossref] [Google Scholar]
[124]Maridaki A, Pampouchidou A, Marias K, Tsiknakis M. Machine learning techniques for automatic depression assessment. In international conference on telecommunications and signal processing 2018 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[125]Oyong I, Utami E, Luthfi ET. Natural language processing and lexical approach for depression symptoms screening of Indonesian twitter user. In international conference on information technology and electrical engineering 2018 (pp. 359-64). IEEE.
[Crossref] [Google Scholar]
[126]Song T, Zheng W, Song P, Cui Z. EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Transactions on Affective Computing. 2018; 11(3):532-41.
[Crossref] [Google Scholar]
[127]Cong Q, Feng Z, Li F, Xiang Y, Rao G, Tao C. XA-BiLSTM: a deep learning approach for depression detection in imbalanced data. In international conference on bioinformatics and biomedicine 2018 (pp. 1624-7). IEEE.
[Crossref] [Google Scholar]
[128]Martínez-borba V, Suso-ribera C, Osma J. The use of information and communication technologies in perinatal depression screening: a systematic review. Cyberpsychology, Behavior, and Social Networking. 2018; 21(12):741-52.
[Crossref] [Google Scholar]
[129]Eichstaedt JC, Smith RJ, Merchant RM, Ungar LH, Crutchley P, Preoţiuc-pietro D, et al. Facebook language predicts depression in medical records. Proceedings of the National Academy of Sciences. 2018; 115(44):11203-8.
[Crossref] [Google Scholar]
[130]Shah A, Morthland M, Scogin F, Presnell A, Dinapoli EA, Decoster J, et al. Audio and computer cognitive behavioral therapy for depressive symptoms in older adults: a pilot randomized controlled trial. Behavior Therapy. 2018; 49(6):904-16.
[Crossref] [Google Scholar]
[131]Tan Q, Cai Y, Li Q, Zhang Y, Tu D. Development and validation of an item bank for depression screening in the Chinese population using computer adaptive testing: a simulation study. Frontiers in Psychology. 2018; 9:1-13.
[Crossref] [Google Scholar]
[132]Basu A, Dasgupta A, Thyagharajan A, Routray A, Guha R, Mitra P. A portable personality recognizer based on affective state classification using spectral fusion of features. IEEE Transactions on Affective Computing. 2018; 9(3):330-42.
[Crossref] [Google Scholar]
[133]Jaiswal S, Song S, Valstar M. Automatic prediction of depression and anxiety from behaviour and personality attributes. In international conference on affective computing and intelligent interaction 2019 (pp. 1-7). IEEE.
[Crossref] [Google Scholar]
[134]Gerych W, Agu E, Rundensteiner E. Classifying depression in imbalanced datasets using an autoencoder-based anomaly detection approach. In international conference on semantic computing 2019 (pp. 124-7). IEEE.
[Crossref] [Google Scholar]
[135]Ma L, Wang Y. Constructing a semantic graph with depression symptoms extraction from twitter. In conference on computational intelligence in bioinformatics and computational biology 2019 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[136]Lam G, Dongyan H, Lin W. Context-aware deep learning for multi-modal depression detection. In ICASSP international conference on acoustics, speech and signal processing 2019 (pp. 3946-50). IEEE.
[Crossref] [Google Scholar]
[137]Kwon H, Kang S, Park W, Park J, Lee Y. Deep learning based pre-screening method for depression with imagery frontal EEG channels. In international conference on information and communication technology convergence 2019 (pp. 378-80). IEEE.
[Crossref] [Google Scholar]
[138]Guo W, Yang H, Liu Z. Deep neural networks for depression recognition based on facial expressions caused by stimulus tasks. In international conference on affective computing and intelligent interaction workshops and demos 2019 (pp. 133-9). IEEE.
[Crossref] [Google Scholar]
[139]De MWC, Granger E, Hadid A. Depression detection based on deep distribution learning. In international conference on image processing 2019 (pp. 4544-8). IEEE.
[Crossref] [Google Scholar]
[140]Pan Z, Ma H, Zhang L, Wang Y. Depression detection based on reaction time and eye movement. In international conference on image processing 2019 (pp. 2184-8). IEEE.
[Crossref] [Google Scholar]
[141]Fang J, Wang T, Li C, Hu X, Ngai E, Seet BC, et al. Depression prevalence in postgraduate students and its association with gait abnormality. IEEE Access. 2019; 7:174425-37.
[Crossref] [Google Scholar]
[142]Yuan Y, Wang Q. Detection model of depression based on eye movement trajectory. In international conference on data science and advanced analytics 2019 (pp. 612-3). IEEE.
[Crossref] [Google Scholar]
[143]Tadesse MM, Lin H, Xu B, Yang L. Detection of depression-related posts in reddit social media forum. IEEE Access. 2019; 7:44883-93.
[Crossref] [Google Scholar]
[144]Sumali B, Mitsukura Y, Tazawa Y, Kishimoto T. Facial landmark activity features for depression screening. In annual conference of the society of instrument and control engineers of Japan 2019 (pp. 1376-81). IEEE.
[Crossref] [Google Scholar]
[145]Kim M, Lim JS. Finding and evaluating suitable contents to recognize depression based on neuro-fuzzy algorithm. In international conference on information and communication technology convergence 2019 (pp. 478-83). IEEE.
[Crossref] [Google Scholar]
[146]Zhou X, Huang P, Liu H, Niu S. Learning content-adaptive feature pooling for facial depression recognition in videos. Electronics Letters. 2019; 55(11):648-50.
[Google Scholar]
[147]Niu M, Tao J, Liu B. Local second-order gradient cross pattern for automatic depression detection. In international conference on affective computing and intelligent interaction workshops and demos 2019 (pp. 128-32). IEEE.
[Crossref] [Google Scholar]
[148]Al-naggar AN, Bamashmos SH, Wadaane M, Abou AM, Hamawy L. Major depressive disorder early detection. In first international conference of intelligent computing and engineering 2019 (pp. 1-7). IEEE.
[Crossref] [Google Scholar]
[149]Kumar SD, Subha DP. Prediction of depression from EEG signal using long short term memory (LSTM). In international conference on trends in electronics and informatics 2019 (pp. 1248-53). IEEE.
[Crossref] [Google Scholar]
[150]Hao F, Pang G, Wu Y, Pi Z, Xia L, Min G. Providing appropriate social support to prevention of depression for highly anxious sufferers. IEEE Transactions on Computational Social Systems. 2019; 6(5):879-87.
[Crossref] [Google Scholar]
[151]Syarif I, Ningtias N, Badriyah T. Study on mental disorder detection via social media mining. In international conference on computing, communications and security 2019 (pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[152]Liang G, Li Y, Liao D, Hu H, Zhang Y, Xu X. The relationship between EEG and depression under induced emotions using VR scenes. In MTT-S international microwave biomedical conference 2019 (pp. 1-4). IEEE.
[Crossref] [Google Scholar]
[153]Ware S, Yue C, Morillo R, Lu J, Shang C, Bi J, et al. Predicting depressive symptoms using smartphone data. Smart Health. 2020.
[Crossref] [Google Scholar]
[154]Gadassi Polack R, Tran TB, Joormann J. “What has been is what will be”? Autobiographical memory and prediction of future events in depression. Cognition and Emotion. 2020; 34(5):1044-51.
[Crossref] [Google Scholar]