Skip to main content
SpringerLink
Log in

Computational Techniques in Prognostic and Data Modelling of Mentally Ill Patients with Special Emphasis on Post-COVID-19 Scenario

  • Chapter
  • First Online:
Predictive Analytics of Psychological Disorders in Healthcare

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 128))

  • 248 Accesses

Abstract

Mental health illnesses and psychological disorders have been on the rise for the past few years. As per WHO (2020) [1], 1 billion people in the world suffer from a mental disorder. The current COVID-19 situation contributed significantly to a rise in the number of patients registering under severe mental illnesses. These disorders, if detected early, can be controlled and treated well. The detection can be done using predictive models, computational and data analytical tools. This chapter focuses on understanding the role of various such models and tools in detection as well as treatment of the concerned mental conditions. The authors tend to focus additionally on the impact of COVID-19 on patients. The chapter consists of predictive and analytical models, inferences drawn from survey-based trials, and literature surveys. Proposed solutions for mental disorders using computational and survey-based methods are also included in the later sections of the chapter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. S.L. James, D. Abate, K.H. Abate et al., 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. Lancet 392(10159), 1789–1858 (2018). http://doi.org/10.1016/S0140-6736(18)32279-7

  2. Centre for Mental Health, Economic and social costs of mental health problems in 2009/10. Centre for Mental Health, London (2010). Retrieved from https://www.centreformentalhealth.org.uk/Handlers/Download.ashx?IDMF=6a98a6da-b9f5-4a07-b88a-067976a0bf5b

  3. D. Becker et al., Predictive modelling in e-mental health: a common language framework. Internet Interv. 12, 57–67 (2018)

    Article  Google Scholar 

  4. A. Kamran Ulhaq, A. Khattak, N. Jamil, M.A. Naeem, F. Mirza, Data analytics in mental healthcare, in Scientific Programming, 2020 (2020), p. 2024160. https://doi.org/10.1155/2020/2024160

  5. N. Titov, B.F. Dear, L.G. Staples, J. Bennett-Levy, B. Klein, R.M. Rapee, C. Shann, D. Richards, G. Andersson, L. Ritterband, C. Purtell, G. Bezuidenhout, L. Johnston, O.B. Nielssen, MindSpot clinic: an accessible, efficient, and effective online treatment service for anxiety and depression. Psychiatr. Serv. 66(10), 1043–1050 (2015). https://doi.org/10.1176/appi.ps.201400477

    Article  Google Scholar 

  6. M. Hoogendoorn, B. Funk. Machine Learning for the Quantified Self (Springer, Berlin, 2017)

    Google Scholar 

  7. S. Kaur, Y. Hasija, Role of computational intelligence against COVID-19, in Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, ed. by K. Raza (Springer, Singapore, 2021), pp. 19–43. http://doi.org/10.1007/978-981-15-8534-0_2

  8. Gender graph suggesting male female susceptibility to mental health concerns, https://www.statista.com/statistics/1115900/adults-with-anxiety-disorders-in-countries-worldwide-by-gender/

  9. L. Duan, G. Zhu, Psychological interventions for people affected by the COVID-19 epidemic. Lancet Psychiatry 7, 300–302 (2020)

    Article  Google Scholar 

  10. S. Galea, R.M. Merchant, N. Lurie, The mental health consequences of COVID-19 and physical distancing: the need for prevention and early intervention. JAMA Intern. Med. 180, 817–818 (2020)

    Article  Google Scholar 

  11. N. Kar, B. Kar, S. Kar, Stress and coping during COVID-19 pandemic: result of an online survey. Psychiatry Res. 295, 113598 (2021)

    Google Scholar 

  12. G. Serafini, B. Parmigiani, A. Amerio, A. Aguglia, L. Sher, M. Amore, The psychological impact of COVID-19 on the mental health in the general population. QJM Int. J. Med. 113, 531–537 (2020)

    Article  Google Scholar 

  13. M. Brooks, COVID-19: mental illness the “inevitable” next pandemic? Medscape—Psychiatry News (2020). Available online: https://www.medscape.com/viewarticle/928756

  14. N. Salari, A. Hosseinian-Far, R. Jalali, A. Vaisi-Raygani, S. Rasoulpoor, M. Mohammadi, S. Rasoulpoor, B. Khaledi-Paveh, Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. Glob. Health 16 (2020)

    Google Scholar 

  15. M. Czeisler, R. Lane, E. Petrosky, J. Wiley, A. Christensen, R. Njai, M. Weawer, Mental health, substance use, and suicidal ideation during the COVID-19 pandemic—the United States, 24–30 June 2020. U.S. Department of Health and Human Services Centers for Disease Control and Prevention. Available online: https://www.cdc.gov/mmwr/volumes/69/wr/mm6932a1.htm

  16. J. Chodkiewicz, J. Miniszewska, E. Krajewska, P. Biliński, Mental health during the second wave of the COVID-19 pandemic—polish studies. Int J Environ. Res. Public Health 18(7), 3423 (2021). http://doi.org/10.3390/ijerph18073423

  17. S.Y. Lim, E.J. Kim, A. Kim, H.J. Lee, H.J. Choi, S.J. Yang, Nutritional factors affecting mental health. CNR 5(3), 143–152 (2016). http://doi.org/10.7762/cnr.2016.5.3.143

  18. M.T. Compton, R.S. Shim, The social determinants of mental health. Focus (Madison) 13(4), 419–425 (2015). https://doi.org/10.1176/appi.focus.20150017

    Article  Google Scholar 

  19. D.V. Dimitrov, Medical internet of things and big data in healthcare. Healthc. Inform. Res. 22(3), 156–163 (2016)

    Article  Google Scholar 

  20. C. Sohrabi, Z. Alsafi, N. O’Neill et al., World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19) [published correction appears in Int. J. Surg. 2020 May; 77:217]. Int. J. Surg. 76, 71–76 (2020). https://doi.org/10.1016/j.ijsu.2020.02.034

    Article  Google Scholar 

  21. S. Goyal, Sentimental analysis of Twitter data using text mining and hybrid classification approach. Int. J. Adv. Res. Ideas Innov. Technol. 2(5), 2454–3132 (2016)

    Google Scholar 

  22. M. Kumar, A. Bala, Analyzing Twitter sentiments through big data, in 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016, pp. 2628–2631

    Google Scholar 

  23. P. Kellmeyer, Big brain data: on the responsible use of brain data from clinical and consumer-directed neurotechnological devices. Neuroethics 11, 1–16 (2018)

    Article  Google Scholar 

  24. S. Monteith, T. Glenn, J. Geddes, M. Bauer, Big data are coming to psychiatry: a general introduction. Int. J. Bipolar Disord. 3(1), 21 (2015)

    Article  Google Scholar 

  25. S. Graham, C. Depp, E.E. Lee, C. Nebeker, X. Tu, H.C. Kim, D.V. Jeste, Artificial intelligence for mental health and mental illnesses: an overview. Curr. Psychiatry Rep. 21(11), 116 (2019). https://doi.org/10.1007/s11920-019-1094-0

    Article  Google Scholar 

  26. V. Arun, V. Prajwal, M. Krishna, B.V. Arunkumar, S.K. Padma, V. Shyam, A boosted machine learning approach for detection of depression, in Proceedings 2018 IEEE Symposium Series on Computational Intelligence SSCI 2018 (2018), pp. 41–47. http://doi.org/10.1109/SSCI.2018.8628945 [CrossRef] [Google Scholar]

  27. S. Zilcha-Mano, S.P. Roose, P.J. Brown, B.R. Rutherford, A machine learning approach to identifying placebo responders in late-life depression trials. Am. J. Geriatr. Psychiatry 26(6), 669–677 (2018). http://doi.org/10.1016/j.jagp.2018.01.001

  28. S.H. Park, K. Han, Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis. Radiology 286(3), 800–809 (2018). https://doi.org/10.1148/radiol.2017171920

    Article  MathSciNet  Google Scholar 

  29. J.W. Kim, K.L. Jones, E.D. Angelo, How to prepare prospective psychiatrists in the era of artificial intelligence. Acad. Psychiatry 1–3 (2019). http://doi.org/10.1007/s40596-019-01025-x

  30. E. Topol, Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, 1st edn. (Basic Books, New York, NY, 2019) [Google Scholar]

    Google Scholar 

  31. I.R. Galatzer-Levy, S. Ma, A. Statnikov, R. Yehuda, A.Y. Shalev, Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD. Transl. Psychiatry 7, e0 (2017)

    Google Scholar 

  32. G. Cho, J. Yim, Y. Choi, J. Ko, S.H. Lee, Review of machine learning algorithms for diagnosing mental illness. Psychiatry Invest. 16(4), 262–269 (2019). http://doi.org/10.30773/pi.2018.12.21.2

  33. M.J. Maenner, M. Yeargin-Allsopp, K. Van Naarden Braun, D.L. Christensen, L.A. Schieve, Development of a machine learning algorithm for the surveillance of autism spectrum disorder. PLoS ONE 11, e0168224 (2016)

    Google Scholar 

  34. C.M. Hatton, L.W. Paton, D. McMillan, J. Cussens, S. Gilbody, P.A. Tiffin, Predicting persistent depressive symptoms in older adults: a machine learning approach to personalised mental healthcare. J. Affect. Disord. (2018). https://doi.org/10.1016/j.jad.2018.12.095

    Article  Google Scholar 

  35. G.M. Reger, M.L. McClure, D. Ruskin, S.P. Carter, M.A. Reger, Integrating predictive modeling into mental health care: an example in suicide prevention. Psychiatric Serv. 70(1), 71–74 (2019/01/01 2018). http://doi.org/10.1176/appi.ps.201800242

  36. W. Bleidorn, C.J. Hopwood, Using machine learning to advance personality assessment and theory. Pers. Soc. Psychol. Rev. 23(2), 190–203 (2019). http://doi.org/10.1177/1088868318772990

    Article  Google Scholar 

  37. A. Furnham, A big five facet analysis of sub-clinical dependent personality disorder (dutifulness). Psychiatry Res. 270, 622–626 (2018)

    Article  Google Scholar 

  38. P. Dhaka, R. Johari, Big data application: study and archival of mental health data, using MongoDB, in Proceedings of the 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India, March 2016, pp. 3228–3232

    Google Scholar 

  39. C. Hill, J.L. Martin, S. Thomson, N. Scott-Ram, H. Penfold, C. Creswell, Navigating the challenges of digital health innovation: considerations and solutions in developing online and smartphone-application-based interventions for mental health disorders. Br. J. Psychiatry 211(2), 65–69 (2017)

    Article  Google Scholar 

  40. R.C. Kessler, S.L. Bernecker, R.M. Bossarte et al., The role of big data analytics in predicting suicide, in Personalized Psychiatry (Springer, Cham, Switzerland, 2019), pp. 77–98

    Google Scholar 

  41. B. Cleland, J. Wallace, R. Bond et al., Insights into antidepressant prescribing using open health data. Big Data Res. 12, 41–48 (2018)

    Article  Google Scholar 

  42. S.S. Rahman, An application of data mining of mental health data, in SAIS 2019 Proceedings (2019), p. 42. https://aisel.aisnet.org/sais2019/42

  43. A. Dawel, Y. Shou, M. Smithson et al., The effect of COVID-19 on mental health and well-being in a representative sample of Australian adults. Front. Psychiatry 11, 579985 (2020)

    Google Scholar 

  44. P.J. Batterham, A.L. Calear, S.M. McCallum, A.R. Morse, M. Banfield, L.M. Farrer, A. Gulliver, N. Cherbuin, R.M. Rodney Harris, Y. Shou, A. Dawel, Trajectories of depression and anxiety symptoms during the COVID-19 pandemic in a representative Australian adult cohort. Med. J. Aust. 214, 462–468 (2021). https://doi.org/10.5694/mja2.51043

    Article  Google Scholar 

  45. M. Costa Graziela, R. Anthony, P. Chyrell et al., Tele-mental health utilization among people with mental illness to access care during the COVID-19 pandemic. Comm. Mental Health J. 57(4), 720–726 (2021). http://doi.org/10.1007/s10597-021-00789-7

  46. K. Kroenke, R.L. Spitzer, J.B.W. Williams, B. Lowe, The patient health questionnaire somatic, anxiety, and depressive symptom scales: a systematic review. Gen. Hosp. Psychiatry 32, 345–359 (2010)

    Article  Google Scholar 

  47. Teens who participate in extracurriculars, get less screen time, have better mental health, https://www.sciencedaily.com/releases/2020/11/201102124849.htm

  48. D. Kwon, M.R. Hodkiewicz, J. Fan, T. Shibutani, M.G. Pecht, IoT-based prognostics and systems health management for industrial applications. IEEE Access 4, 3659–3670 (2016). https://doi.org/10.1109/ACCESS.2016.2587754

    Article  Google Scholar 

  49. M.S.R.M. Saufi, Z.A. Ahmad, M.H. Lim, M.S. Leong, A review on signal processing techniques for bearing diagnostics. Int. J. Mech. Eng. Technol. 8, 327–337 (2017)

    Google Scholar 

  50. S. Kaur, Y. Hasija, Prognostic modeling with the internet of healthcare things applications, in Advanced Prognostic Predictive Modelling in Healthcare Data Analytics (Springer Singapore, Germany, 2021), p. 129

    Google Scholar 

  51. H.R. Wong, S.L. Weiss, J.S. Giulian Jr. et al., Testing the prognostic accuracy of the updated pediatric sepsis biomarker risk model. PLoS One 9(1), e86242 (2014). http://doi.org/10.1371/journal.pone.0086242

  52. S. Qazi, K. Tanveer, K. El-Bahnasy, K. Raza, Chapter 10—From Telediagnosis to Tele Treatment: The Role of Computational Biology and Bioinformatics in Tele-Based Healthcare, ed. By H.D. Jude, V.E.B.T.-T.T. Balas (Academic Press, 2019), pp. 153–169. http://doi.org/10.1016/B978-0-12-816948-3.00010-6

  53. M.A. Jenkins-Guarnieri, L.D. Pruitt, D.D. Luxton, K. Johnson, Patient perceptions of telemental health: systematic review of direct comparisons to in-person psychotherapeutic treatments. Telemedicine J. e-health Official J. Am. Telemedicine Assoc. 21(8), 652–660 (2015). https://doi.org/10.1089/tmj.2014.0165

  54. J.H. Wright, R. Caudill, Remote treatment delivery in response to the COVID-19 pandemic. Psychother. Psychosom. 89(3), 130–132 (2020). https://doi.org/10.1159/000507376

    Article  Google Scholar 

  55. A.C. Smith, E. Thomas, C.L. Snoswell, H. Haydon, A. Mehrotra, J. Clemensen, L.J. Caffery, Telehealth for global emergencies: Implications for coronavirus disease 2019 (COVID-19). J. Telemedicine Telecare 1357633X20916567 (2020)

    Google Scholar 

  56. J. Torous, K. Jän Myrick, N. Rauseo-Ricupero, J. Firth, Digital mental health and COVID-19: using technology today to accelerate the curve on access and quality tomorrow. JMIR Mental Health 7(3), e18848 (2020). http://doi.org/10.2196/18848

  57. S. Chakrabarti, Usefulness of telepsychiatry: a critical evaluation of videoconferencing-based approaches. World J. Psychiatry 5(3), 286–304 (2015). https://doi.org/10.5498/wjp.v5.i3.286

    Article  Google Scholar 

  58. H. Mahmoud, E. Whaibeh, B. Mitchell, Ensuring successful telepsychiatry program implementation: critical components and considerations. Curr. Treat. Options Psychiatry 7(2), 186–197 (2020). http://doi.org/10.1007/s40501-020-00208-w

    Article  Google Scholar 

  59. E.R. Dorsey, E.J. Topol, State of telehealth. N. Engl. J. Med. 375(2), 154–161 (2016). http://doi.org/10.1056/NEJMra1601705

  60. T. Greenhalgh, J. Wherton, S. Shaw, C. Morrison, Video consultations for covid-19. BMJ 368, m998 (2020). http://doi.org/10.1136/bmj.m998

  61. P.K. Khosla, M. Mittal, L.M. Goyal, K. Chachra, Mitigate the impact of Covid-19: telehealth, in Predictive and Preventive Measures for Covid-19 Pandemic. Algorithms for Intelligent Systems, ed. by P.K. Khosla, M. Mittal, D. Sharma, L.M. Goyal (Springer, Singapore, 2021). http://doi.org/10.1007/978-981-33-4236-1_1

  62. P.K. Khosla, M. Mittal, D. Sharma, L.M. Goyal (eds.), Predictive and Preventive Measures for Covid-19 Pandemic (Springer, Singapore, 2021)

    Google Scholar 

  63. https://www.icmr.gov.in/pdf/covid/techdoc/PSC_COVID_patients_v1_30042021.pdf

Download references

Author information

Authors and Affiliations

  1. Department of Biotechnology, Delhi Technological University, Delhi, India

    Simran Kaur

  2. Defence Institute of Psychological Research, DRDO, Delhi, India

    Sandhya Verma & R. K. Sokhi

Authors
  1. Simran Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sandhya Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. K. Sokhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Simran Kaur .

Editor information

Editors and Affiliations

  1. Delhi Skill and Entrepreneurship University, New Delhi, India

    Mamta Mittal

  2. Department of Computer Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad, India

    Lalit Mohan Goyal

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kaur, S., Verma, S., Sokhi, R.K. (2022). Computational Techniques in Prognostic and Data Modelling of Mentally Ill Patients with Special Emphasis on Post-COVID-19 Scenario. In: Mittal, M., Goyal, L.M. (eds) Predictive Analytics of Psychological Disorders in Healthcare. Lecture Notes on Data Engineering and Communications Technologies, vol 128. Springer, Singapore. https://doi.org/10.1007/978-981-19-1724-0_5

Download citation

Publish with us

Policies and ethics

Search

Navigation