KETCH: A Knowledge-Enhanced Transformer-Based Approach to Suicidal Ideation Detection from Social Media Content

Published Online:https://doi.org/10.1287/isre.2021.0619

References

  • Adams T, Clark C, Crowell V, Duffy K, Green M (2017) The mental health benefits of having dogs on college campuses. Modern Psych. Stud. 22(2):7.Google Scholar
  • Adrian M, Lyon AR (2018) Social media data for online adolescent suicide risk identification: Considerations for integration within platforms, clinics, and schools. Moreno M, Radovic A, eds. Technology and Adolescent Mental Health (Springer, Berlin, Heidelberg), 155–170.CrossrefGoogle Scholar
  • Aladağ A, Muderrisoglu S, Akbas N, Zahmacioglu O (2018) Detecting suicidal ideation on forums: Proof-of-concept study. J. Medical Internet Res. 20(6):e215.CrossrefGoogle Scholar
  • Allen K, Bagroy S, Krishnamurti T (2019) ConvSent at CLPsych 2019 Task A: Using post-level sentiment features for suicide risk prediction on Reddit. Proc. 6th Workshop Comput. Linguistics Clin. Psych. (Association for Computational Linguistics, Kerrville, TX).Google Scholar
  • Ambalavanan A, Jagtap P, Adhya S, Devarakonda M (2019) Using contextual representations for suicide risk assessment from Internet forums. Proc. Sixth Workshop Comput. Linguistics Clin. Psych. (Association for Computational Linguistics, Kerrville, TX), 172–176.Google Scholar
  • Annapragada A, Donaruma-Kwoh M, Annapragada A, Starosolski Z (2021) A natural language processing and deep learning approach to identify child abuse from pediatric electronic medical records. PLoS One 16(2):e0247404.CrossrefGoogle Scholar
  • Asim M, Wasim M, Khan M, Mahmood W, Abbasi H (2018) A survey of ontology learning techniques and applications. Database 2018:bay101.CrossrefGoogle Scholar
  • Ayabakan S, Bardhan I, Zheng Z (2017) The impact of health information sharing on duplicate testing. Management Inform. Systems Quart. 41(4):1083–1103.CrossrefGoogle Scholar
  • Bakken N, Gunter W (2012) Self-cutting and suicidal ideation among adolescents: Gender differences in the causes and correlates of self-injury. Deviant Behav. 33(5):339–356.CrossrefGoogle Scholar
  • Bandhakavi A, Wiratunga N, Padmanabhan D, Massie S (2017) Lexicon based feature extraction for emotion text classification. Pattern Recognition Lett. 93:133–142.Google Scholar
  • Barak A, Miron O (2005) Writing characteristics of suicidal people on the Internet: A psychological investigation of emerging social environments. Suicide Life Threatening Behav. 35(5):507–524.CrossrefGoogle Scholar
  • Bitew S, Bekoulis G, Deleu J, Sterckx L, Zaporojets K, Demeester T, Develder C (2019) Predicting suicide risk from online postings in Reddit The UGent-IDLab submission to the CLPysch 2019 Shared Task A. Proc. 6th Workshop Comput. Linguistics Clin. Psych. (Association for Computational Linguistics, Kerrville, TX) 158–161.Google Scholar
  • Blei DM, Ng AY, Jordan M (2003) Latent Dirichlet allocation. J. Machine Learn. Res. 3(Jan):993–1022.Google Scholar
  • Bucur AM, Cosma A, Dinu LP (2021) Early risk detection of pathological gambling, self harm and depression using bersultt. Proc. Conf. Labs Evaluation Forum (CEUR-WS.org).Google Scholar
  • Calvo R, Milne D, Hussain S, Christensen H (2017) Natural language processing in mental health applications using nonclinical texts. Natural Language Engrg. 23(5):649–685.CrossrefGoogle Scholar
  • Cao L, Zhang H, Feng L (2020) Building and using personal knowledge graph to improve SID on social media. IEEE Trans. Multimedicine 24:87–102.CrossrefGoogle Scholar
  • Chau M, Li TMH, Wong PWC, Xu JJ, Yip PSF, Chen H (2020) Finding people with emotional distress in online social media: A design combining machine learning and rule-based classification. Management Inform. Systems Quart. 44(2):933–955.CrossrefGoogle Scholar
  • Chawla N, Bowyer KW, Hall L, Kegelmeyer W (2002) SMOTE: Synthetic minority over-sampling technique. J. Artificial Intelligence Res. 16:321–357.CrossrefGoogle Scholar
  • Cheng PGF, Ramos RM, Bitsch JÁ, Jonas SM, Ix T, See PLQ, Wehrle K, et al. (2016) Psychologist in a pocket: Lexicon development and content validation of a mobile-based app for depression screening. JMIR Mhealth Uhealth 4(3):e88.CrossrefGoogle Scholar
  • Choi E, Bahadori MT, Song L, Stewart WF, Sun J (2017) GRAM: Graph-based attention model for healthcare representation learning. 23rd ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York).Google Scholar
  • Choudhury MD, Gamon M, Counts S, Horvitz E (2013) Predicting depression via social media. Kiciman N, Ellison B, Hogan B, Resnick P, Soboroff I, eds. Proc. Internat. AAAI Conf. Web Social Media, vol. 7(1) (Association for the Advancement of Artificial Intelligence, Palo Alto, CA), 128–137.Google Scholar
  • Choudhury MD, Kiciman E, Dredze M, Coppersmith G, Kumar M (2016) Discovering shifts to suicidal ideation from mental health content in social media. Proc. CHI’16: CHI Conf. Human Factors Comput. Systems (Association for Computing Machinery, New York), 2098–2110.Google Scholar
  • Conner A, Azrael D, Miller M (2019) Suicide case-fatality rates in the United States, 2007 to 2014: A nationwide population-based study. Ann. Internal Medicine 171(12):885–895.CrossrefGoogle Scholar
  • Coppersmith G, Leary R, Crutchley P (2018) Natural language processing of social media as screening for suicide risk. Biomedical Inform. Insights 10:1–11.CrossrefGoogle Scholar
  • Coppersmith G, Leary R, Whyne E, Wood T (2015) Quantifying suicidal ideation via language usage on social media. Proc. Joint Statistics Meetings (American Statistical Association, Alexandria, VA).Google Scholar
  • D’zurilla TJ, Goldfried MR (1971) Problem solving and behavior modification. J. Abnormal Psych. 78(1):107.CrossrefGoogle Scholar
  • Davis J, Goadrich M (2006) The relationship between precision-recall and ROC curves. Proc. 23rd Internat. Conf. Machine Learn. (Association for Computing Machinery, New York), 233–240.Google Scholar
  • Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics Human Language Technologies (Association for Computational Linguistics, Kerrville, TX), 4171–4186.Google Scholar
  • Dhaoui C, Webster C, Tan L (2017) Social media sentiment analysis: Lexicon vs. machine learning. J. Consumer Marketing 34(6):480–488.CrossrefGoogle Scholar
  • Diniz E, Fontenele JE, de Oliveira AC, Bastos VH, Teixeira S, Rabêlo R, Calçada DB, et al. (2022) Boamente: A natural language processing-based digital phenotyping tool for smart monitoring of suicidal ideation. Health Care (Don Mills) 2022(10):698.Google Scholar
  • Ethayarajh K (2019) How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings. Preprint, submitted September 2, https://arxiv.org/abs/1909.00512.Google Scholar
  • Etudo U, Yoon VY (2023) Ontology-based information extraction for labeling radical online content using distant supervision. Inform. Systems Res. 35(1):203–225.Google Scholar
  • Fügener A, Grahl J, Gupta A, Ketter W (2021) Will humans-in-the-loop become borgs? Merits and pitfalls of working with AI. Management Inform. Systems Quart. 45(3):1527–1556.CrossrefGoogle Scholar
  • Gaur M, Alambo A, Sain J, Kursuncu U (2019) Knowledge-aware assessment of severity of suicide risk for early intervention. Ling L, White R, eds. Proc. World Wide Web Conf. (ACM, New York), 514–525.Google Scholar
  • Gerald K, Amber Y, Ann M, Sam R (2021) Avoiding an oppressive future of machine learning: A design theory for emancipatory assistants. Management Inform. Systems Quart. 45(1):371–396.CrossrefGoogle Scholar
  • Gill T, Hevner A (2013) A fitness-utility model for design science research. ACM Trans. Management Inform. Systems 4(2):1–24.CrossrefGoogle Scholar
  • Gnambs T, Kaspar K (2015) Disclosure of sensitive behaviors across self-administered survey modes: A meta-analysis. Behav. Res. Methods 47(4):1237–1259.CrossrefGoogle Scholar
  • Guan L, Hao B, Cheng Q, Yip Paul SF, Zhu T (2015) Identifying Chinese microblog users with high suicide probability using Internet-based profile and linguistic features: Classification model. JMIR Mental Health 2(2):e17.CrossrefGoogle Scholar
  • Guthrie E, Kapur N, Mackway-Jones K, Chew-Graham C, Moorey J, Mendel E, Marino-Francis F, et al. (2001) Randomized controlled trial of brief psychological intervention after deliberate self-poisoning. BMJ 323(7305):135–138.CrossrefGoogle Scholar
  • Haque R, Islam N, Islam M, Ahsan MM (2022) A comparative analysis on SID using NLP, machine, and deep learning. Technologies (Basel) 10:57.CrossrefGoogle Scholar
  • Haque F, Nur R, Jahan S, Mahmud Z, Shah F (2020) A transformer based approach to detect suicidal ideation using pretrained language models. Proc. 23rd Internat. Conf. Comput. Inform. Tech. (IEEE, Piscataway, NJ).Google Scholar
  • Haque A, Reddi V, Giallanza T, Farkaš I, Masulli P, Otte S, Wermter S, eds. (2021) Deep learning for suicide and depression identification with unsupervised label correction. Artificial Neural Networks and Machine Learning (Springer International Publishing, Cham, Switzerland).Google Scholar
  • Huang Y, Goh T, Liew C (2007) Hunting suicide notes in Web 2.0: Preliminary findings. Proc. 9th IEEE Internat. Sympos. Multimedia Workshops (IEEE, Piscataway, NJ).Google Scholar
  • Huang X, Zhang L, Chiu D, Liu T, Li X, Zhu T (2014) Detecting suicidal ideation in Chinese microblogs with psychological lexicons. Proc. Internat. Conf. Ubiquitous Intelligence Comput. Internat. Conf. Autonomic Trusted Comput. Internat. Conf. Scalable Comput. Comm. ITS Associated Workshops (IEEE, Piscataway, NJ), 844–849.Google Scholar
  • Ji S, Li X, Huang Z, Cambria E (2022b) Suicidal ideation and mental disorder detection with attentive relation networks. Neural Comput. Appl. 34(13):10309–10319.CrossrefGoogle Scholar
  • Ji S, Long G, Pan S, Zhu T, Jiang J, Wang S (2019a) Detecting suicidal ideation with data protection in online communities. Proc. Internat. Conf. Database Systems Adv. Appl. (Springer International Publishing, Cham, Switzerland), 225–229.Google Scholar
  • Ji S, Pan S, Li X, Cambria E, Long G, Huang Z (2021) SID: A review of machine learning methods and applications. IEEE Trans. Comput. Soc. Syst. 8(1):214–226.CrossrefGoogle Scholar
  • Ji S, Zhang T, Ansari L, Fu J, Tiwari P, Cambria E (2022a) Mentalbert: Publicly available pretrained language models for mental healthcare. Proc. Thirteenth Language Resources Evaluation Conf. (European Language Resources Association, Paris), 7184–7190.Google Scholar
  • Ji S, Long G, Pan S, Zhu T, Jiang J, Wang S, Li X (2019b) Knowledge transferring via model aggregation for online social care. Preprint, submitted May 19, https://arxiv.org/abs/1905.07665.Google Scholar
  • Johnson J (2021) Chapter 12 – Human decision-making is rarely rational. Johnson J, ed. Designing with the Mind in Mind, 3rd ed. (Morgan Kaufmann Publishers, Burlington, MA), 203–223.Google Scholar
  • Kim A, Jeon S, Cho S, Shin Y, Park J (2021) Comparison of the factors for suicidal ideation and suicide attempt: A comprehensive examination of stress, view of life, mental health, and alcohol use. Asian J. Psychiatry 65:102844.CrossrefGoogle Scholar
  • Klonsky D, May AM, Saffer BY (2016) Suicide, suicide attempts, and suicidal ideation. Annu. Rev. Clinical Psych. 12:307–330.CrossrefGoogle Scholar
  • Kroenke K, Spitzer R, Williams J (2001) The PHQ-9. J. General Internal Medicine 16(9):606–613.CrossrefGoogle Scholar
  • Le CC, Prasad PWC, Alsadoon A, Pham L, Elchouemi A (2019) Text classification: Naïve bayes classifier with sentiment Lexicon. IAENG Internat. J. Comput. Sci. 46(2):141–148.Google Scholar
  • Lee J, Lee M, Liao S, Chang C, Sung S, Chiang H, Tai C (2010) Prevalence of suicidal ideation and associated risk factors in the general population. J. Formos Medical Assoc. 109(2):138–147.CrossrefGoogle Scholar
  • Li C, Xing W (2021) Natural language generation using deep learning to support MOOC learners. Internat. J. Artificial Intelligence Ed. 31:186–214.CrossrefGoogle Scholar
  • Li Z, Zhou J, An Z, Cheng W, Hu B (2022) Deep hierarchical ensemble model for suicide detection on imbalanced social media data. Entropy 24(4):1–15.CrossrefGoogle Scholar
  • Li J, Yan Z, Lin Z, Liu X, Leong HV, Yu N, Li Q (2021) Suicide ideation detection on social media during Covid-19 via adversial and multi-task learning. Gao Y, Liu A, Tao X, Chen J, eds. Proc. 5th Internat. Joint Conf. Asia-Pacific Web Web-Age Inform. Management (Springer, Berlin, Heidelberg), 140–145.Google Scholar
  • Lin Y, Chen H, Brown RA, Li S (2017) Healthcare predictive analytics for risk profiling in chronic care: A Bayesian multitask learning approach. Management Inform. Systems Quart. 41(2):473–495.CrossrefGoogle Scholar
  • Liu X, Zhang B, Susarlia A, Padman R (2020) Go to YouTube and call me in the morning: Use of social media for chronic conditions. Management Inform. Systems Quart. 44(1b):257–283.CrossrefGoogle Scholar
  • Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, et al. (2019) RoBERTa: A robustly optimized BERT pretraining approach. Preprint, submitted July 26, https://arxiv.org/abs/1907.11692.Google Scholar
  • Ljubic B, Roychoudhury S, Cao X, Pavlovski M, Obradovic S, Nair R, Glass L, et al. (2020) Influence of medical domain knowledge on deep learning for Alzheimer’s disease prediction. Comput. Methods Programs Biomedicine 197:105765.CrossrefGoogle Scholar
  • Martínez-Castaño R, Htait A, Azzopardi L, Moshfeghi Y (2020) Early risk detection of self-harm and depression severity using BERT-based transformers: iLab at CLEF eRisk 2020. CEUR Workshop Proc. (CEUR-WS.org), 2696.Google Scholar
  • Matero M, Idnani A, Son Y, Giorgi S, Vu H, Zamani M, Limbachiya P, et al. (2019) Suicide risk assessment with multi-level dual-context language and BERT. Proc. 6th Workshop Comput. Linguistics Clinical Psych. (Association for Computational Linguistics, Kerrville, TX), 39–44.Google Scholar
  • McCarthy MJ (2010) Internet monitoring of suicide risk in the population. J. Affective Disorders 122(3):277–279.CrossrefGoogle Scholar
  • McHugh CM, Corderoy A, Ryan CJ, Hickie IB, Large MM (2019) Association between suicidal ideation and suicide: Meta-analyses of odds ratios, sensitivity, specificity and positive predictive value. BJPsych Open 5(2):e18.CrossrefGoogle Scholar
  • Mikolov T, Corrado G, Chen K, Dean J (2013) Efficient estimation of word representations in vector space. Proc. Internat. Conf. Learn. Representations (ICLR, Appleton, WI), 1–12.Google Scholar
  • Mohammad S (2018) Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 English words. Proc. 56th Annual Meeting Assoc. Comput. Linguistics (Association for Computational Linguistics, Kerrville, TX).Google Scholar
  • Mohammadi E, Amini H, Kosseim L (2019) CLaC at CLPsych 2019: Fusion of neural features and predicted class probabilities for suicide risk assessment based on online posts. Proc. Sixth Workshop Comput. Linguistics Clinical Psych. (Association for Computational Linguistics, Kerrville, TX), 34–38.Google Scholar
  • Morales M, Belitz D, Chernova N, Dey P, Theisen T (2019) An investigation of deep learning systems for suicide risk assessment. Proc. Sixth Workshop Comput. Linguistics Clinical Psych. (Association for Computational Linguistics, Kerrville, TX), 177–181.Google Scholar
  • Neuman Y, Cohen Y, Assaf D, Kedma G (2012) Proactive screening for depression through metaphorical and automatic text analysis. Artificial Intelligence Medicine 56(1):19–25.CrossrefGoogle Scholar
  • Nielsen M, Nielsen G, Notelaers G, Einarsen S (2015) Workplace bullying and suicidal ideation: A 3-wave longitudinal Norwegian study. Amer. J. Public Health 105(11):e23–e28.CrossrefGoogle Scholar
  • Olson R, Urbanowicz R, Andrews PC, Lavender N, Kidd L, Moore J (2016) Automating biomedical data science through tree-based pipeline optimization. Squillero G, Burelli P, eds. Applications of Evolutionary Computation. EvoApplications 2016, Lecture Notes in Computer Science, vol. 9597 (Springer, Cham, Switzerland), 123–137.Google Scholar
  • Park M, McDonald DW, Cha M (2013) Perception differences between the depressed and nondepressed users in Twitter. Proc. 7th Internat. AAAI Conf. Weblogs Social Media (AAAI, Palo Alto, CA), 476–485.Google Scholar
  • Patterson A, Holden R (2012) Psychache and suicide ideation among men who are homeless: A test of Shneidman’s model. Suicide Life Threatening Behav. 42(2):147–156.CrossrefGoogle Scholar
  • Perry Y, Werner-Seidler A, Calear A, Christensen H (2016) Web-based and mobile suicide prevention interventions for young people: A systematic review. J. Canadian Acad. Child Adolescent Psychiatry 25(2):73–79.Google Scholar
  • Peterson C, Miller GF, Sarah Beth LB, Florence C (2021) Economic cost of injury—United States, 2019. MMWR Morbidity Mortality Weekly Rep. 70(48):1655–1659.CrossrefGoogle Scholar
  • Podkorytov M, Bis D, Liu X (2021) How can the [mask] know? The sources and limitations of knowledge in BERT. Proc. Internat. Joint Conf. Neural Networks (IEEE, Piscataway, NJ).Google Scholar
  • Pompili M (2019) Critical appraisal of major depression with suicidal ideation. Ann. General Psychiatry 18:7.CrossrefGoogle Scholar
  • Ríssola E, Ramírez-Cifuentes D, Freire A, Crestani F (2019) Suicide risk assessment on social media: USI-UPF at the CLPsych 2019 shared task. Proc. Sixth Workshop Comput. Linguistics Clinical Psych. (Association for Computational Linguistics, Kerrville, TX), 167–171.Google Scholar
  • Robinson J, Hetrick S, Cox G, Bendall S, Yung A, Pirkis J (2015) The safety and acceptability of delivering an online intervention to secondary students at risk for suicide: Findings from a pilot study. Early Intervention Psychiatry 9(6):498–506.CrossrefGoogle Scholar
  • Ross EL, Zuromski KL, Reis BY, Nock MK, Kessler RC, Smoller JW (2021) Accuracy requirements for cost-effective suicide risk prediction among primary care patients in the US. JAMA Psychiatry 78(6):1–9.CrossrefGoogle Scholar
  • Roy A, Pan S (2021) Incorporating medical knowledge in BERT for clinical relation extraction. Proc. Conf. Empirical Methods Natural Language Processing (Association for Computational Linguistics, Kerrville, TX), 5357–5366.Google Scholar
  • Rudd MD, Berman AL, Joiner TE, Nock MK, Silverman MM, Mandrusiak M, Van Orden K, Witte T (2006) Warning signs for suicide: Theory, research, and clinical applications. Suicide Life Threatening Behav. 36:255–262.CrossrefGoogle Scholar
  • Saifee D, Zheng Z, Bardhan I, Lahiri A (2020) Are online physician reviews reliable indicators of clinical outcomes? A focus on chronic disease management. Inform. Systems Res. 31(4):1282–1300.LinkGoogle Scholar
  • Santesteban-Echarri O, Rice S, Wadley G, Lederman R, D’Alfonso S, Russon P, Chambers R, et al. (2017) A next-generation social media-based relapse prevention intervention for youth depression: Qualitative data on user experience outcomes for social networking, safety, and clinical benefit. Internet Interventions 9:65–73.CrossrefGoogle Scholar
  • Sarsam S, Al-Samarraie H, Alzahrani A, Alnumay W, Smith AP (2021) A lexicon-based approach to detecting suicide-related messages on Twitter. Biomedical Signal Processing Control 65:102355.CrossrefGoogle Scholar
  • Sawhney R, Joshi H, Gandhi S, Shah R (2021) Toward ordinal suicide ideation detection on social media. Proc. 14th ACM Internat. Conf. Web Search Data Mining (Association for Computing Machinery, New York), 22–30.Google Scholar
  • Sawhney R, Manchanda P, Singh R, Aggarwal S (2018) A computational approach to feature extraction for identification of suicidal ideation in tweets. Proc. ACL Student Res. Workshop (Association for Computational Linguistics, Kerrville, TX), 91–98.Google Scholar
  • Schoene A, Turner A, Mel G, Dethlefs N (2021) Hierarchical multiscale recurrent neural networks for detecting suicide notes. IEEE Trans. Affective Comput. 14(1):153–164.Google Scholar
  • Shepard DS, Gurewich D, Lwin AK, Reed GA, Silverman MM (2016) Suicide and Suicidal attempts in the United States: Costs and policy implications. Suicide Life Threatening Behav. 46(3):352–362.CrossrefGoogle Scholar
  • Shing H, Nair S, Zirikly A, Friedenberg M, Daume H III, Resnik P (2018) Expert, crowdsourced, and machine assessment of suicide risk via online postings. Loveys K, Niederhoffer K, Prud’hommeaux E, Resnik R, Resnik P, eds. Proc. 5th Workshop Comput. Linguistics Clinical Psych.: From Keyboard Clinic (Association for Computational Linguistics, Kerrville, TX), 25–36.Google Scholar
  • Silverman M, Berman A, Sanddal N, O’Carroll P, Joiner T (2007) Rebuilding the tower of Babel: A revised nomenclature for the study of suicide and suicide behaviors. Part 2: Suicide-related ideations, communications, and behaviors. Suicide Life Threatening Behav. 37(3):264–277.CrossrefGoogle Scholar
  • Son Y, Bayas N, Schwartz A (2018) Causal explanation analysis on social media. Riloff E, Chiang D, Hockenmaier J, Tsujii J, eds. Proc. Conf. Empirical Methods Natural Language Processing (Association for Computational Linguistics, Kerrville, TX), 3350–3359.Google Scholar
  • Svetitic J, De Leo D (2012) The hypothesis of a continuum in suicidality: A discussion on its validity and practical implications. Mental Illness 4:e15.Google Scholar
  • Tadesse M, Lin H, Xu B, Yang L (2020) Detection of suicide ideation in social media forums using deep learning. Algorithms (Basel) 13(7):1–19.Google Scholar
  • Van den Broek E, Sergeeva A, Huysman M (2021) When the machine meets the expert: An ethnography of developing AI for hiring. Management Inform. Systems Quart. 45(3):1557–1580.CrossrefGoogle Scholar
  • Varathan K, Talib N (2014) Suicide detection system based on Twitter. Proc. Sci. Inform. Conf. (IEEE, Piscataway, NJ).Google Scholar
  • Vioules M, Moulahi B, Aze J, Bringay S (2018) Detection of suicide-related posts in Twitter data streams. IBM J. Res. Development 62(1):7:1–7:12.CrossrefGoogle Scholar
  • Wang M, Swaraj S, Chung D, Stanton C, Kapur N, Large M (2019) Meta-analysis of suicide rates among people discharged from non-psychiatric settings after presentation with suicidal thoughts or behaviors. Acta Psychiatry Scand. 139:472–483.CrossrefGoogle Scholar
  • Wasserman D, Losue M, Wuestefeld A, Carli V (2021) Adaptation of evidence-based suicide prevention strategies during and after the COVID-19 pandemic. World Psychiatry 19(3):294–306.Google Scholar
  • Weiner MG, Sheikh W, Lehmann HP (2018) Interactive cost-benefit analysis: Providing real-world financial context to predictive analytics. AMIA Annu. Sympos. Proc. (AMIA, Washington, DC),1076–1083.Google Scholar
  • Wu J, Zheng Z, Zhao JL (2021) FairPlay: Detecting and deterring online customer misbehavior. Inform. Systems Res. 32(4):1323–1346.LinkGoogle Scholar
  • Xie J, Liu X, Zeng D, Fang X (2022) Understanding medication nonadherence from social media: A sentiment-enriched deep learning approach. Management Inform. Systems Quart. 46(1):341–372.CrossrefGoogle Scholar
  • Yang K, Lau RY, Abbasi A (2023a) Getting personal: A deep learning artifact for text-based measurement of personality. Inform. Systems Res. 34(1):194–222.LinkGoogle Scholar
  • Yang Y, Qin Y, Fan Y, Zhang Z (2023b) Unlocking the power of voice for financial risk prediction: A theory-driven deep learning design approach. Management Inform. Systems Quart. 47(1):63–96.CrossrefGoogle Scholar
  • Yates M (2004) The developmental psychology of self-injurious behavior: Compensatory regulation in posttraumatic adaptation. Clinical Psych. Rev. 24:35–74.CrossrefGoogle Scholar
  • Yates A, Cohan A, Goharian N (2017) Depression and self-harm risk assessment in online forums. Proc. Conf. Empirical Methods Natural Language Processing (Association for Computational Linguistics, Kerrville, TX).Google Scholar
  • Zhang W, Ram S (2020) A comprehensive analysis of triggers and risk factors for asthma based on machine learning and large heterogeneous data sources. Management Inform. Systems Quart. 44(1b):305–349.CrossrefGoogle Scholar
  • Zhao N, Jiao D, Bai S, Zhu T (2016) Evaluating the validity of Simplified Chinese version of LIWC in detecting psychological expressions in short texts on Social Network Services. PLoS One 11(6):e0157947.CrossrefGoogle Scholar
  • Zhou L, Pan S, Wang J, Vasilakos AV (2017) Machine learning on big data: Opportunities and challenges. Neural Comput. 237:350–361.Google Scholar
  • Zirikly A, Resnik P, Uzuner Ö, Hollingshead K (2019) CLPsych 2019 shared task: Predicting the degree of suicide risk in Reddit posts. Proc. Sixth Workshop Comput. Linguistics Clinical Psych. (Association for Computational Linguistics, Kerrville, TX).Google Scholar
  • Zortea T, Brenna C, Joyce M, McClelland H, Tippett M, Tran MM, Arensman E, et al. (2020) The impact of infectious disease-related public health emergencies on suicide, suicidal behavior, and suicidal thoughts. Crisis 42(6):474–487.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.