Automatic depression score estimation with word embedding models

1948 Prince, 2007, No health without mental health, Lancet, 370, 859, 10.1016/S0140-6736(07)61238-0 World Health Organization, 2005 Hollon, 2002, Treatment and prevention of depression, Psychol Sci Public Interest, 3, 39, 10.1111/1529-1006.00008 Kessler, 2009, The global burden of mental disorders: an update from the WHO World Mental Health (WMH) surveys, Epidemiologia E Psichiatria Sociale, 18, 23, 10.1017/S1121189X00001421 Saxena, 2015, Comprehensive mental health action plan 2013–2020, EMHJ-Eastern Mediterr Health J, 21, 461, 10.26719/2015.21.7.461 Kessler, 1994, Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States: results from the National Comorbidity Survey, Arch Gen Psychiatry, 51, 8, 10.1001/archpsyc.1994.03950010008002 Patel, 2016 Pierce, 2020, Mental health before and during the COVID-19 pandemic: a longitudinal probability sample survey of the UK population, Lancet Psychiatry, 7, 883, 10.1016/S2215-0366(20)30308-4 Duarte, 2009, Cognitive–behavioral group therapy is an effective treatment for major depression in hemodialysis patients, Kidney Int, 76, 414, 10.1038/ki.2009.156 Alonso, 2007, Population level of unmet need for mental healthcare in Europe, Br J Psychiatry, 190, 299, 10.1192/bjp.bp.106.022004 Picardi, 2016, A randomised controlled trial of the effectiveness of a program for early detection and treatment of depression in primary care, J Affect Disord, 198, 96, 10.1016/j.jad.2016.03.025 Halfin, 2007, Depression: the benefits of early and appropriate treatment, Am J Managed Care, 13, S92 Rost, 2004, The effect of improving primary care depression management on employee absenteeism and productivity, a randomized trial, Med Care, 42, 1202, 10.1097/00005650-200412000-00007 Smarr, 2011, Arthritis Care Res, 63, S454, 10.1002/acr.20556 Kroenke, 2001, The PHQ-9: validity of a brief depression severity measure, J Gen Intern Med, 16, 606, 10.1046/j.1525-1497.2001.016009606.x Zigmond, 1983, The hospital anxiety and depression scale, Acta Psychiatr Scand, 67, 361, 10.1111/j.1600-0447.1983.tb09716.x Eaton, 2004 Hamilton, 1980, Rating depressive patients, J Clin Psychiatry Beck, 1996, Beck depression inventory–II, Psychol Assess Dozois, 1998, A psychometric evaluation of the Beck Depression Inventory–II, Psychol Assess, 10, 83, 10.1037/1040-3590.10.2.83 Beck, 1988, Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation, Clin Psychol Rev, 8, 77, 10.1016/0272-7358(88)90050-5 Sanchez-Villegas, 2008, Validity of a self-reported diagnosis of depression among participants in a cohort study using the Structured Clinical Interview for DSM-IV (SCID-I), BMC Psychiatry, 8, 1, 10.1186/1471-244X-8-43 U.S. Department of Health and Human Services, 2012 Cameron, 2011, Measuring depression severity in general practice: discriminatory performance of the PHQ-9, HADS-D, and BDI-II, Br J Gen Pract, 61, e419, 10.3399/bjgp11X583209 Bowling, 2005, Mode of questionnaire administration can have serious effects on data quality, J Public Health, 27, 281, 10.1093/pubmed/fdi031 De Choudhury M, Counts S, Horvitz E. Social media as a measurement tool of depression in populations. In: Proceedings of the 5th annual ACM web science conference, 2013. p. 47–56. Colineau, 2010, Talking about your health to strangers: understanding the use of online social networks by patients, New Rev Hypermedia Multimedia, 16, 141, 10.1080/13614568.2010.496131 Losada, 2016, A test collection for research on depression and language use, 28 Aragón ME, Monroy APL, González-Gurrola LC, Montes M. Detecting depression in social media using fine-grained emotions. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), 2019. p. 1481–6. Prieto, 2014, Twitter: a good place to detect health conditions, PLoS One, 9, 10.1371/journal.pone.0086191 De Choudhury M, Gamon M, Counts S, Horvitz E. Predicting depression via social media. In: Proceedings of the international AAAI conference on web and social media, Vol. 7, 2013. De Choudhury M, Counts S, Horvitz EJ, Hoff A. Characterizing and predicting postpartum depression from shared facebook data. In: Proceedings of the 17th ACM conference on computer supported cooperative work & social computing, 2014. p. 626–38. Stordal, 2003, The association between age and depression in the general population: a multivariate examination, Acta Psychiatr Scand, 107, 132, 10.1034/j.1600-0447.2003.02056.x Lienemann, 2013, Persuading people with depression to seek help: Respect the boomerang, Health Commun, 28, 718, 10.1080/10410236.2012.712091 Barney, 2006, Stigma about depression and its impact on help-seeking intentions, Aust N Z J Psychiatry, 40, 51, 10.1080/j.1440-1614.2006.01741.x Pennebaker, 1997 Pennebaker, 2003, Psychological aspects of natural language use: Our words, our selves, Ann Rev Psychol, 54, 547, 10.1146/annurev.psych.54.101601.145041 Campbell, 2003, The secret life of pronouns: Flexibility in writing style and physical health, Psychol Sci, 14, 60, 10.1111/1467-9280.01419 Rude, 2004, Language use of depressed and depression-vulnerable college students, Cogn Emot, 18, 1121, 10.1080/02699930441000030 MacAvaney, 2018 Cohan, 2018 De Choudhury M, Kiciman E. The language of social support in social media and its effect on suicidal ideation risk. In: Proceedings of the international AAAI conference on web and social media, Vol. 11, 2017. Desmet, 2018, Online suicide prevention through optimised text classification, Inform Sci, 439, 61, 10.1016/j.ins.2018.02.014 Mitchell M, Hollingshead K, Coppersmith G. Quantifying the language of schizophrenia in social media. In: Proceedings of the 2nd workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality, 2015. p. 11–20. Wang T, Brede M, Ianni A, Mentzakis E. Detecting and characterizing eating-disorder communities on social media. In: Proceedings of the tenth ACM international conference on web search and data mining, 2017. p. 91–100. Coppersmith G, Dredze M, Harman C, Hollingshead K. From ADHD to SAD: Analyzing the language of mental health on Twitter through self-reported diagnoses. In: Proceedings of the 2nd workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality, 2015. p. 1–10. De Choudhury M, Counts S, Horvitz E. Social media as a measurement tool of depression in populations. In: Proceedings of the 5th annual ACM web science conference, 2013. p. 47–56. De Choudhury M, De S. Mental health discourse on reddit: Self-disclosure, social support, and anonymity. In: Eighth international AAAI conference on weblogs and social media. Chen X, Sykora MD, Jackson TW, Elayan S. What about mood swings: Identifying depression on twitter with temporal measures of emotions. In: Companion proceedings of the the web conference 2018, 2018. p. 1653–60. Kawachi, 2001, Social ties and mental health, J Urban Health, 78, 458, 10.1093/jurban/78.3.458 Ortega-Mendoza, 2022, Revealing traces of depression through personal statements analysis in social media, Artif Intell Med, 123, 10.1016/j.artmed.2021.102202 Jamil, 2017, Monitoring tweets for depression to detect at-risk users, 32 Tlelo-Coyotecatl, 2022, Depression recognition in social media based on symptoms’ detection, Procesamiento Del Lenguaje Natural, 68, 25 Nguyen, 2022 Mikolov, 2013, Efficient estimation of word representations in vector space, Proceedings of Workshop at ICLR, 2013 Pennington J, Socher R, Manning CD. Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 2014. p. 1532–43. Devlin, 2019, BERT: Pre-training of deep bidirectional transformers for language understanding, 4171 Brown, 2020, Language models are few-shot learners, Adv Neural Inf Process Syst, 33, 1877 Raffel, 2020, Exploring the limits of transfer learning with a unified text-to-text transformer, J Mach Learn Res, 21, 140:1 Kenter T, De Rijke M. Short text similarity with word embeddings. In: Proceedings of the 24th ACM international on conference on information and knowledge management, 2015. p. 1411–20. Giatsoglou, 2017, Sentiment analysis leveraging emotions and word embeddings, Expert Syst Appl, 69, 214, 10.1016/j.eswa.2016.10.043 Alsentzer, 2019, Publicly available clinical BERT embeddings, 72 Lee, 2020, BioBERT: a pre-trained biomedical language representation model for biomedical text mining, Bioinformatics, 36, 1234, 10.1093/bioinformatics/btz682 Martínez-Castaño, 2020, Early risk detection of self-harm and depression severity using BERT-based transformers: iLab at CLEF eRisk 2020, Early Risk Predict Internet Bucur, 2021, Early risk detection of pathological gambling, self-harm and depression using BERT Coppersmith G, Dredze M, Harman C, Hollingshead K, Mitchell M. CLPsych 2015 shared task: Depression and PTSD on Twitter. In: Proceedings of the 2nd workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality, 2015. p. 31–39. Losada, 2017, eRISK 2017: CLEF lab on early risk prediction on the internet: experimental foundations, 346 Losada DE, Crestani F, Parapar J. Overview of eRisk 2018: Early Risk Prediction on the Internet (extended lab overview). In: Proceedings of the 9th international conference of the CLEF association, CLEF, 2018. p. 1–20. Losada, 2019, Overview of erisk 2019 early risk prediction on the internet, 340 Losada, 2020, Erisk 2020: Self-harm and depression challenges, 557 Burdisso, 2019, A text classification framework for simple and effective early depression detection over social media streams, Expert Syst Appl, 133, 182, 10.1016/j.eswa.2019.05.023 Rijen Pv, Teodoro D, Naderi N, Mottin L, Knafou J, Ruch P. Data-driven approach for measuring the severity of the signs of depression using reddit posts. In: Proceedings of CLEF (conference and labs of the evaluation forum) 2019 working notes, no. CONFERENCE, 9-12 September 2019, 2019. Abed-Esfahani, 2019, Transfer learning for depression: Early detection and severity prediction from social media postings Burdisso, 2021, Using text classification to estimate the depression level of reddit users, J Comput Sci Technol, 21, 10.24215/16666038.21.e1 Maupomé, 2020, Early mental health risk assessment through writing styles, topics and neural models Oliveira, 2020 Uban, 2020, Deep learning architectures and strategies for early detection of self-harm and depression level prediction, Vol. 2696, 1 Steer, 1986, Applications of the beck depression inventory, 123 Joshi, 2015, Likert scale: Explored and explained, Curr J Appl Sci Technol, 396 Robertson, 2009 Mikolov, 2018, Advances in pre-training distributed word representations Trask, 2015 Zhang, 2020 Rajpurkar, 2016, SQuAD: 100,000+ questions for machine comprehension of text, 2383 Cer D, Yang Y, Kong S-y, Hua N, Limtiaco N, John RS et al. Universal sentence encoder for English. In: Proceedings of the 2018 conference on empirical methods in natural language processing: system demonstrations, 2018. p. 169–74. Strong, 2007, Evaluation of the continuum of gambling problems using the DSM-IV, Addiction, 102, 713, 10.1111/j.1360-0443.2007.01789.x Garner, 2004, Eating disorder inventory-3 (EDI-3)