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StudentSADD: Rapid Mobile Depression and Suicidal Ideation Screening of College Students during the Coronavirus Pandemic

Published: 07 July 2022 Publication History

Abstract

The growing prevalence of depression and suicidal ideation among college students further exacerbated by the Coronavirus pandemic is alarming, highlighting the need for universal mental illness screening technology. With traditional screening questionnaires too burdensome to achieve universal screening in this population, data collected through mobile applications has the potential to rapidly identify at-risk students. While prior research has mostly focused on collecting passive smartphone modalities from students, smartphone sensors are also capable of capturing active modalities. The general public has demonstrated more willingness to share active than passive modalities through an app, yet no such dataset of active mobile modalities for mental illness screening exists for students. Knowing which active modalities hold strong screening capabilities for student populations is critical for developing targeted mental illness screening technology. Thus, we deployed a mobile application to over 300 students during the COVID-19 pandemic to collect the Student Suicidal Ideation and Depression Detection (StudentSADD) dataset. We report on a rich variety of machine learning models including cutting-edge multimodal pretrained deep learning classifiers on active text and voice replies to screen for depression and suicidal ideation. This unique StudentSADD dataset is a valuable resource for the community for developing mobile mental illness screening tools.

Supplementary Material

tlachac-1 (tlachac-1.zip)
Supplemental movie, appendix, image and software files for, StudentSADD: Rapid Mobile Depression and Suicidal Ideation Screening of College Students during the Coronavirus Pandemic

References

[1]
Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijaya-narasimhan. 2016. Youtube-8m: A large-scale video classification benchmark. arXiv preprint arXiv:1609.08675 (2016).
[2]
AJMC Staff. 2021. A Timeline of COVID-19 Developments in 2020. The American Journal of Managed Care (2021).
[3]
Meysam Asgari, Izhak Shafran, and Lisa B Sheeber. 2014. Inferring clinical depression from speech and spoken utterances. In 2014 IEEE international workshop on Machine Learning for Signal Processing (MLSP). IEEE, 1--5.
[4]
Nasser F BinDhim, Ahmed M Shaman, Lyndal Trevena, Mada H Basyouni, Lisa G Pont, and Tariq M Alhawassi. 2015. Depression screening via a smartphone app: cross-country user characteristics and feasibility. Journal of the American Medical Informatics Association 22, 1 (2015), 29--34.
[5]
Mehdi Boukhechba, Alexander R Daros, Karl Fua, Philip I Chow, Bethany A Teachman, and Laura E Barnes. 2018. DemonicSalmon: monitoring mental health and social interactions of college students using smartphones. Smart Health 9 (2018), 192--203.
[6]
Kyle Bowen and Matthew D Pistilli. 2012. Student preferences for mobile app usage. Research Bulletin)(Louisville, CO: EDUCAUSE Center for Applied Research, forthcoming), available from http://www. educause. edu/ecar (2012).
[7]
Hanshu Cai, Yiwen Gao, Shuting Sun, Na Li, Fuze Tian, Han Xiao, Jianxiu Li, Zhengwu Yang, Xiaowei Li, Qinglin Zhao, et al. 2020. MODMA dataset: a Multi-model Open Dataset for Mental-disorder Analysis. arXiv (2020), arXiv-2002.
[8]
Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. 1293--1304.
[9]
Stevie Chancellor and Munmun De Choudhury. 2020. Methods in predictive techniques for mental health status on social media: a critical review. NPJ digital medicine 3, 1 (2020), 1--11.
[10]
Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACD Sigkdd International conference on Knowledge Discovery and Data Mining. 785--794.
[11]
R Conrad, H Rayala, M Menon, and K Vora. 2020. Universities' response to supporting mental health of college students during the COVID-19 pandemic. Psychiatric Times (2020).
[12]
Nicholas Cummins, Julien Epps, Michael Breakspear, and Roland Goecke. 2011. An investigation of depressed speech detection: Features and normalization. In Twelfth Annual Conference of the International Speech Communication Association.
[13]
Nicholas Cummins, Stefan Scherer, Jarek Krajewski, Sebastian Schnieder, Julien Epps, and Thomas F Quatieri. 2015. A review of depression and suicide risk assessment using speech analysis. Speech Communication 71 (2015), 10--49.
[14]
Mark É Czeisler, Rashon I Lane, Emiko Petrosky, Joshua F Wiley, Aleta Christensen, Rashid Njai, Matthew D Weaver, Rebecca Robbins, Elise R Facer-Childs, Laura K Barger, et al. 2020. Mental health, substance use, and suicidal ideation during the COVID-19 pandemic---United States, June 24-30, 2020. Morbidity and Mortality Weekly Report 69, 32 (2020), 1049.
[15]
Munmun De Choudhury, Scott Counts, Eric J. Horvitz, and Aaron Hoff. 2014. Characterizing and Predicting Postpartum Depression from Shared Facebook Data. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work Social Computing. ACM, 626--638.
[16]
Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. 2013. Predicting depression via social media. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 7.
[17]
Koen Demyttenaere, Ronny Bruffaerts, J Posada-Villa, I Gasquet, V Kovess, JPeal Lepine, MC Angermeyer, SD Bernert, P Morosini, G Polidori, et al. 2004. Prevalence, severity, and unmet need for treatment of mental disorders in the World Health Organization World Mental Health Surveys. Jama 291, 21 (2004), 2581--2590.
[18]
David DeVault, Ron Artstein, Grace Benn, Teresa Dey, Ed Fast, Alesia Gainer, Kallirroi Georgila, Jon Gratch, Arno Hartholt, Margaux Lhommet, et al. 2014. SimSensei Kiosk: A virtual human interviewer for healthcare decision support. In Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems. 1061--1068.
[19]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[20]
Daniel Di Matteo, Kathryn Fotinos, Sachinthya Lokuge, Julia Yu, Tia Sternat, Martin A Katzman, and Jonathan Rose. 2020. The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study. JMIR Form Res 4, 8 (2020).
[21]
Ada Dogrucu, Alex Perucic, Anabella Isaro, Damon Ball, Ermal Toto, Elke A Rundensteiner, Emmanuel Agu, Rachel Davis-Martin, and Edwin Boudreaux. 2020. Moodable: On feasibility of instantaneous depression assessment using machine learning on voice samples with retrospectively harvested smartphone and social media data. Smart Health (2020), 100--118.
[22]
Dominic B Dwyer, Peter Falkai, and Nikolaos Koutsouleris. 2018. Machine learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology 14 (2018), 91--118.
[23]
Daniel Eisenberg, Sarah Ketchen Lipson, et al. 2020. The Healthy Minds Study: 2018-2019 Data Report. (2020).
[24]
Daniel Eisenberg, Sarah Ketchen Lipson, Justin Heinze, et al. 2020. The Healthy Minds Study: Fall 2020 Data Report. (2020).
[25]
Ronald M Epstein, Paul R Duberstein, Mitchell D Feldman, Aaron B Rochlen, Robert A Bell, Richard L Kravitz, Camille Cipri, Jennifer D Becker, Patricia M Bamonti, and Debora A Paterniti. 2010. " I didn't know what was wrong:" How people with undiagnosed depression recognize, name and explain their distress. Journal of General Internal Medicine 25, 9 (2010), 954--961.
[26]
Florian. Eyben. 2016. Real-time Speech and Music Classification by Large Audio Feature Space Extraction. Springer International Publishing.
[27]
Ethan Fast, Binbin Chen, and Michael S Bernstein. 2016. Empath: Understanding topic signals in large-scale text. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 4647--4657.
[28]
J Firth, N Siddiqi, A Koyanagi, D Siskind, S Rosenbaum, C Galletly, et al. 2019. A blueprint for protecting physical health in people with mental illness: directions for health promotion, clinical services and future research. Lancet Psychiatry (2019).
[29]
Ricardo Flores, ML Tlachac, Ermal Toto, and Elke A Rundensteiner. 2021. Depression Screening Using Deep Learning on Follow-up Questions in Clinical Interviews. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 595--600.
[30]
Felix A Gers, Jürgen Schmidhuber, and Fred Cummins. 2000. Learning to forget: Continual prediction with LSTM. Neural computation 12, 10 (2000), 2451--2471.
[31]
Walter Gerych, Emmanuel Agu, and Elke Rundensteiner. 2019. Classifying depression in imbalanced datasets using an autoencoder-based anomaly detection approach. In 2019 IEEE 13th International Conference on Semantic Computing (ICSC). IEEE, 124--127.
[32]
Jonathan Gratch, Ron Artstein, Gale M Lucas, Giota Stratou, Stefan Scherer, Angela Nazarian, Rachel Wood, Jill Boberg, David DeVault, Stacy Marsella, et al. 2014. The distress analysis interview corpus of human and computer interviews. In Language Resources and Evaluation. CiteSeer, 3123--3128.
[33]
S. Guntuku, D. Yaden, M. Kern, L. Ungar, and J. Eichstaedt. 2017. Detecting Depression and Mental Illness on Social Media: An Integrative Review. Current Opinion in Behavioral Sciences 18 (2017).
[34]
Aron Halfin. 2007. Depression: the benefits of early and appropriate treatment. American Journal of Managed Care 13, 4 (2007), S92.
[35]
Hartford HealthCare. 2020. These Age Groups Most Affected by COVID-Related Depression, Anxiety. HartFord HealthCare: News Detail (2020).
[36]
Holly Hedegaard, Sally Curtin, and Margaret Warner. 2020. Increase in Suicide Mortality in the United States, 1999--2018. NCHS Data Brief No. 366 (2020). https://www.cdc.gov/nchs/data/databriefs/db362-h.pdf.
[37]
Shawn Hershey, Sourish Chaudhuri, Daniel PW Ellis, Jort F Gemmeke, Aren Jansen, R Channing Moore, Manoj Plakal, Devin Platt, Rif A Saurous, Bryan Seybold, et al. 2017. CNN architectures for large-scale audio classification. In 2017 ieee international conference on acoustics, speech and signal processing (icassp). IEEE, 131--135.
[38]
Zhaocheng Huang, Julien Epps, Dale Joachim, and Michael Chen. 2018. Depression Detection from Short Utterances via Diverse Smartphones in Natural Environmental Conditions. In INTERSPEECH. 3393--3397.
[39]
Jeremy F Huckins, Alex W DaSilva, Weichen Wang, Elin Hedlund, Courtney Rogers, Subigya K Nepal, Jialing Wu, Mikio Obuchi, Eilis I Murphy, Meghan L Meyer, et al. 2020. Mental health and behavior of college students during the early phases of the COVID-19 pandemic: longitudinal smartphone and ecological momentary assessment study. Journal of medical Internet research 22, 6 (2020), e20185.
[40]
ET Isometsä. 2001. Psychological autopsy studies-a review. European psychiatry 16, 7 (2001), 379--385.
[41]
Spencer L James, Degu Abate, Kalkidan Hassen Abate, Solomon M Abay, Cristiana Abbafati, Nooshin Abbasi, Hedayat Abbastabar, Foad Abd-Allah, Jemal Abdela, Ahmed Abdelalim, et al. 2018. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392, 10159 (2018), 1789--1858.
[42]
Saumya Joseph. 2019. Depression, anxiety rising among US college students. Reuters Health News (2019).
[43]
Kurt Kroenke, Robert L Spitzer, and Janet BW Williams. 2001. The PHQ-9: validity of a brief depression severity measure. Journal of General Internal Medicine 16, 9 (2001), 606--613.
[44]
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017. A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130 (2017).
[45]
Zhenyu Liu, Dongyu Wang, Lan Zhang, and Bin Hu. 2020. A Novel Decision Tree for Depression Recognition in Speech. arXiv preprint arXiv:2002.12759 (2020).
[46]
S. Loria. 2018. TextBlob: Simplified Text Processing. (2018). https://textblob.readthedocs.io/en/dev/.
[47]
Peter F Lovibond and Sydney H Lovibond. 1995. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behaviour research and therapy 33, 3 (1995), 335--343.
[48]
Thomas E Joiner Mary E Duffy, Jean M Twenge. 2019. Trends in Mood and Anxiety Symptoms and Suicide-Related Outcomes Among U.S. Undergraduates, 2007-2018: Evidence From Two National Surveys. Journal of Adolescent Health (2019).
[49]
Mario Gennaro Mazza, Rebecca De Lorenzo, Caterina Conte, Sara Poletti, Benedetta Vai, Irene Bollettini, Elisa Maria Teresa Melloni, Roberto Furlan, Fabio Ciceri, Patrizia Rovere-Querini, et al. 2020. Anxiety and depression in COVID-19 survivors: Role of inflammatory and clinical predictors. Brain, behavior, and immunity 89 (2020), 594--600.
[50]
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011).
[51]
Pew Research Center. 2019. Smartphone ownership is growing rapidly around the world but not always equally. https://www.pewresearch.org/global/2019/02/05/smartphone-ownership-is-growing-rapidly-around-the-world-but-not-always-equally/
[52]
Lenore Sawyer Radloff. 1977. The CES-D scale: A self-report depression scale for research in the general population. Applied psychological measurement 1, 3 (1977), 385--401.
[53]
Benjamin J Ricard, Lisa A Marsch, Benjamin Crosier, and Saeed Hassanpour. 2018. Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram. Journal of Medical Internet Research (2018).
[54]
Mariana Rodrigues Makiuchi, Tifani Warnita, Kuniaki Uto, and Koichi Shinoda. 2019. Multimodal fusion of BERT-CNN and gated CNN representations for depression detection. In AVEC. 55--63.
[55]
John Rooksby, Alistair Morrison, and Dave Murray-Rust. 2019. Student perspectives on digital phenotyping: The acceptability of using smartphone data to assess mental health. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1--14.
[56]
A John Rush, Madhukar H Trivedi, Hicham M Ibrahim, Thomas J Carmody, Bruce Arnow, Daniel N Klein, John C Markowitz, Philip T Ninan, Susan Kornstein, Rachel Manber, et al. 2003. The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biological psychiatry 54, 5 (2003), 573--583.
[57]
Margot L Savoy and David T O'Gurek. 2016. Screening your adult patients for depression. Family practice management 23, 2 (2016), 16--20.
[58]
Deepa L. Sekhar, Eric W. Schaefer, James G. Waxmonsky, Leslie R. Walker-Harding, Krista L. Pattison, Alissa Molinari, Perri Rosen, and Jennifer L. Kraschnewski. 2021. Screening in High Schools to Identify, Evaluate, and Lower Depression Among Adolescents: A Randomized Clinical Trial. JAMA Network Open 4, 11 (2021), e2131836-e2131836.
[59]
Saskia Senn, M L Tlachac, Ricardo Flores, and Elke Rundensteiner. Accepted. Ensembles of BERT for Depression Classification. In 44nd International Conference of IEEE Engineering in Medicine and Biology Society (EMBC).
[60]
Albert L Siu, Kirsten Bibbins-Domingo, David C Grossman, Linda Ciofu Baumann, Karina W Davidson, Mark Ebell, Francisco AR García, Matthew Gillman, Jessica Herzstein, Alex R Kemper, et al. 2016. Screening for depression in adults: US Preventive Services Task Force recommendation statement. Jama 315, 4 (2016), 380--387.
[61]
Kimberlee Speakman. 2021. Hate Crimes In U.S. Reach Highest Levels In 12 Years, FBI Says. Forbes (2021).
[62]
ML Tlachac, Katherine Dixon-Gordon, and Elke Rundensteiner. 2021. Screening for Suicidal Ideation with Text Messages. In 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 1--4.
[63]
ML Tlachac, Veronica Melican, Miranda Reisch, and Elke Rundensteiner. 2021. Mobile depression screening with time series of text logs and call logs. In 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 1--4.
[64]
ML Tlachac, Ermal Toto, Joshua Lovering, Rimsha Kayastha, Nina Taurich, and Elke Rundensteiner. 2021. Emu: Early mental health uncovering framework and dataset. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 1311--1318.
[65]
M L Tlachac, Ricardo Flores, Miranda Reisch, Katie Houskeeper, and Elke Rundensteiner. Accepted. DepreST-CAT: Retrospective Smartphone Call and Text Logs Collected During the COVID-19 Pandemic to Screen for Mental Illnesses. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Accepted).
[66]
M. L. Tlachac and Elke Rundensteiner. 2020. Depression Screening from Text Message Reply Latency. In International Conferences of the IEEE Engineering in Medicine and Biology Society. 5490--5493.
[67]
M. L. Tlachac and Elke Rundensteiner. 2020. Screening for Depression with Retrospectively Harvested Private versus Public Text. IEEE Journal of Biomedical and Health Informatics 24, 11 (2020).
[68]
M. L. Tlachac, Adam Sargent, Ermal Toto, Randy Paffenroth, and Elke Rundensteiner. 2020. Topological Data Analysis to Engineer Features from Audio Signals for Depression Detection. In 19th IEEE International Conference on Machine Learning and Applications (ICMLA).
[69]
Ermal Toto, ML Tlachac, Francis Lee Stevens, and Elke A Rundensteiner. 2020. Audio-based Depression Screening using Sliding Window Sub-clip Pooling. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 791--796.
[70]
Ermal Toto, M. L. Tlachac, and Elke Rundensteiner. 2021. AudiBERT: A Deep Transfer Learning Multimodal Classification Framework for Depression Screening. In 30th ACM International Conference on Information and Knowledge Management (CIKM) Applied Research Track.
[71]
M. Valstar, J. Gratch, B. Schuller, F. Ringeval, D. Lalanne, M. Torres, S. Scherer, G. Stratou, R. Cowie, and M. Pantic. 2016. Avec 2016: Depression, mood, and emotion recognition workshop and challenge. In Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge. ACM.
[72]
Michel Valstar, Jonathan Gratch, Björn Schuller, Fabien Ringeval, Denis Lalanne, Mercedes Torres Torres, Stefan Scherer, Giota Stratou, Roddy Cowie, and Maja Pantic. 2016. Avec 2016: Depression, mood, and emotion recognition workshop and challenge. In Proceedings of the 6th international workshop on audio/visual emotion challenge. 3--10.
[73]
Michel Valstar, Björn Schuller, Kirsty Smith, Florian Eyben, Bihan Jiang, Sanjay Bilakhia, Sebastian Schnieder, Roddy Cowie, and Maja Pantic. 2013. AVEC 2013: the continuous audio/visual emotion and depression recognition challenge. In Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge. 3--10.
[74]
Fabian Wahle, Tobias Kowatsch, Elgar Fleisch, Michael Rufer, Steffi Weidt, et al. 2016. Mobile sensing and support for people with depression: a pilot trial in the wild. JMIR mHealth and uHealth 4, 3 (2016), e5960.
[75]
Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T Campbell. 2014. StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 3--14.
[76]
Shweta Ware, Chaoqun Yue, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, and Bing Wang. 2020. Predicting depressive symptoms using smartphone data. Smart Health 15 (2020), 1--16.
[77]
Mark D Weist, Marcia Rubin, Elizabeth Moore, Steven Adelsheim, and Gordon Wrobel. 2007. Mental health screening in schools. Journal of School Health 77, 2 (2007), 53--58.
[78]
Thomas Wolf, Julien Chaumond, Lysandre Debut, Victor Sanh, Clement Delangue, Anthony Moi, Pierric Cistac, Morgan Funtowicz, Joe Davison, Sam Shleifer, et al. 2020. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 38--45.
[79]
Xuhai Xu, Prerna Chikersal, Afsaneh Doryab, Daniella K Villalba, Janine M Dutcher, Michael J Tumminia, Tim Althoff, Sheldon Cohen, Kasey G Creswell, J David Creswell, Jennifer Mankoff, and Anind K Dey. 2019. Leveraging routine behavior and contextually-filtered features for depression detection among college students. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 3 (2019), 1--33.
[80]
Anthony Zhang. 2017. Speech Recognition. https://pypi.org/project/SpeechRecognition/
[81]
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015. Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books. In The IEEE International Conference on Computer Vision (ICCV).

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  • (2024)Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety ScreeningProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435548:1(1-28)Online publication date: 6-Mar-2024
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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 6, Issue 2
July 2022
1551 pages
EISSN:2474-9567
DOI:10.1145/3547347
Issue’s Table of Contents
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Publication History

Published: 07 July 2022
Published in IMWUT Volume 6, Issue 2

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Author Tags

  1. digital phenotype
  2. mental health assessment
  3. mobile health
  4. voice recordings

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  • Chilean National Agency for Research and Development
  • National Science Foundation
  • US Department of Education
  • Fulbright Foreign Student Program

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  • (2024)Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety ScreeningProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435548:1(1-28)Online publication date: 6-Mar-2024
  • (2024)Investigating Generalizability of Speech-based Suicidal Ideation Detection Using Mobile PhonesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314527:4(1-38)Online publication date: 12-Jan-2024
  • (2024)Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health AssessmentBiomedical Materials & Devices10.1007/s44174-023-00150-42:2(778-810)Online publication date: 22-Feb-2024
  • (2024)MindWell: A Dataset Related to Mental HealthProceedings of the NIELIT's International Conference on Communication, Electronics and Digital Technology10.1007/978-981-97-3601-0_34(477-487)Online publication date: 31-Jul-2024
  • (2023)Automated Construction of Lexicons to Improve Depression Screening With Text MessagesIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2022.320334527:6(2751-2759)Online publication date: Jun-2023
  • (2022)Text Generation to Aid Depression Detection: A Comparative Study of Conditional Sequence Generative Adversarial Networks2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020224(2804-2813)Online publication date: 17-Dec-2022
  • (2022)Early Mental Health Uncovering with Short Scripted and Unscripted Voice RecordingsDeep Learning Applications, Volume 410.1007/978-981-19-6153-3_4(79-110)Online publication date: 26-Nov-2022

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