Electronic Healthcare System: Mental Disorder Assessment and Intervention with Self-Treatment Using Rule-Based Techniques

  • Nurnadiah ZamriEmail author
  • Lazim Abdullah
  • Mohd Asrul Hery Ibrahim
Part of the Signals and Communication Technology book series (SCT)


Almost one million people worldwide die by suicide each year. One of the main reasons is due to depression. Life stresses, high depressions, and anxiety are commonly prevalent in mental health problems. Early detection and recognition are in need to allow better treatment and prevention on mental disorders as well as other complications. However, some patients skip their checkup routine due to multiple hospital procedures and long-waiting process. Motivated with the fog computing as a recent technological advancement, this chapter aims to facilitate a new version of an online system on electronic Mental Assessment and Self-Treatment System (e-MAST) for all patients. This system provides patients with stress questionnaires, anxiety questionnaires, and depressive symptoms questionnaires generated using rule-based techniques. Besides, the sum of answers from the patients will be calculated using weighted sum method. This system offers life stress controls and self-treatment techniques while awaiting professional help. This system helps to increase the scientific community’s awareness of mental health and creates an opportunity to embrace a healthy generation of people. Also, this system can be used at all times, anywhere, and can be benefited by all toward smart hospital ideas.


Mental disorder Early recognition and detection Self-treatment Rule-based techniques Smart hospital 


  1. 1.
    Mental Health Foundation. (2016). Fundamental facts about mental health 2016 (Vol. 89). London: Mental Health Foundation. Retrieved from Scholar
  2. 2.
    Disorders, B. (2011). Understanding mental illness a guide to brain disorders, medication, and therapy.Google Scholar
  3. 3.
    Vos, T., Allen, C., Arora, M., Barber, R. M., Brown, A., Carter, A., et al. (2016). Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: A systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1545–1602. Scholar
  4. 4.
    Nebhinani, N., & Kuppili, P. P. (2018). Young people and mental health in a changing world. Journal of Indian Association for Child and Adolescent Mental Health, 14(4), 1–14.Google Scholar
  5. 5.
    Skinner, D., Kendall, H., Skinner, H. M., & Campbell, C. (2019). Mental health simulation: Effects on students’ anxiety and examination scores. Clinical Simulation in Nursing, 35, 33–37. Scholar
  6. 6.
    Gravenhorst, F., Muaremi, A., Bardram, J., Grünerbl, A., Mayora, O., Wurzer, G., et al. (2015). Mobile phones as medical devices in mental disorder treatment: An overview. Personal and Ubiquitous Computing, 19(2), 335–353. Scholar
  7. 7.
    Kumari, S., Tanwar, S., & Tyagi, N. (2018). Fog computing for Healthcare 4.0 environment: Opportunities and challenges. Computers & Electrical Engineering, 72, 1–13.CrossRefGoogle Scholar
  8. 8.
    Vijayakumar, V., Malathi, D., Subramaniyaswamy, V., Saravanan, P., & Logesh, R. (2019). Fog computing-based intelligent healthcare system for the detection and prevention of mosquito-borne diseases. Computers in Human Behavior, 100, 275–285.CrossRefGoogle Scholar
  9. 9.
    McShane, S., & Glinow, M. V. (2018). Organizational behavior (8th ed.). New York, NY: McGrawHill.Google Scholar
  10. 10.
    Ogawa, H., Yamaguchi, Y., Shimada, H., Takakura, H., Akiyama, M., & Yagi, T. (2017). Malware originated HTTP traffic detection utilizing cluster appearance ratio. In International conference on information networking (pp. 248–253). Da Nang: IEEE. Scholar
  11. 11.
    International Society for the Study of Trauma. (2011). Guidelines for treating dissociative identity disorder in adults, third revision. Journal of Trauma and Dissociation, 12(2), 115–187. Scholar
  12. 12.
    Foley, T., & Woollard, J. (2019). The digital future of mental healthcare and its workforce. Retrieved from
  13. 13.
    Cullen, K. (2018). Kevin Cullen (May 2018).Google Scholar
  14. 14.
    Werbeloff, N., Osborn, D. P. J., Patel, R., Taylor, M., Stewart, R., Broadbent, M., & Hayes, J. F. (2018). The camden & Islington research database: Using electronic mental health records for research. PLoS One, 13(1), 1–13. Scholar
  15. 15.
    Children & Young People ’ s Mental Health in the Digital Age. (2018). Oecd, 16. Retrieved from
  16. 16.
    Teles, A. S., Rocha, A., da Silva, E., Silva, F. J., Lopes, J. C., O’Sullivan, D., Van de Ven, P., & Endler, M. (2017). Enriching mental health mobile assessment and intervention with situation awareness. Sensors (Switzerland), 17(1), 1–22. Scholar
  17. 17.
    Zamri, N., Mamat, A. R., Iryani, S., Saany, A., & Yasmi, M. F. (2017). Diagnosis of mental disorder and stress self-treatment using rule-based technique. World Applied Sciences Journal, 35(7), 1204–1209. Scholar
  18. 18.
    Knickman, J., Krishnan, K. R. R., Pincus, H. A., Blanco, C., Blazer, D. G., Coye, M. J., et al. (2016). Improving access to effective care for people who have mental health and substance use disorders: A vital direction for health and health care. NAM Perspectives, 6(9).
  19. 19.
    Bakker, D., Hons, B. P., Kazantzis, N., Rickwood, D., Hons, B. A., Rickard, N., & Hons, B. (n.d.). Mental health smartphone apps: Review and evidence-based recommendations for future developments. JMIR Mental Health, 3, 1–31.
  20. 20.
    Bradford, S., & Rickwood, D. (2015). Young people’s views on electronic mental health assessment: Prefer to type than talk? Journal of Child and Family Studies, 24(5), 1213–1221. Scholar
  21. 21.
    Royal Australian College of General Practitioners. (2015). A guide for GPs e-Mental health: A guide for GPs. East Melbourne, VIC: Royal Australian College of General Practitioners. Retrieved from Scholar
  22. 22.
    Bouchard, S., Campbell, R., Dal Grande, E., Ferdinand, M., Hadjistavropoulos, H., Hopkins, C., … Draper, J. (2014). Health Canada, First Nations Inuit Health Branch). Retrieved from
  23. 23.
    López-robledo, Y. M., López-robledo, D. M., Torres-garcía, V., & Santiago-medina, M. (2014). Electronic medical record: Exploring benefits and barriers perceived by mental health providers University of Puerto Rico in Ponce. American International Journal of Contemporary Research, 4(11), 51–57.Google Scholar
  24. 24.
    Government, A. (n.d.). Emstrat.Google Scholar
  25. 25.
    Donker, T., Petrie, K., Proudfoot, J., Clarke, J., Birch, M. R., & Christensen, H. (2013). Smartphones for smarter delivery of mental health programs: A systematic review. Journal of Medical Internet Research, 15(11), 1–13. Scholar
  26. 26.
    Deziel, M., Olawo, D., Truchon, L., & Golab, L. (2013). Analyzing the mental health of engineering students using classification and regression. In Proceedings of the 6th international conference on educational data mining (pp. 228–231). Memphis, TN: International Educational Data Mining Society. Retrieved from Scholar
  27. 27.
    Kroenke, K., Spitzer, R. L., Williams, J. B. W., & Löwe, B. (2010). The patient health questionnaire somatic, anxiety, and depressive symptom scales: A systematic review. General Hospital Psychiatry, 32(4), 345–359. Scholar
  28. 28.
    Richardson, L. P., Rockhill, C., Russo, J. E., Grossman, D. C., Richards, J., McCarty, C., et al. (2010). Evaluation of the PHQ-2 as a brief screen for detecting major depression among adolescents. Pediatrics, 125(5), 1–9. Scholar
  29. 29.
    Butler, M., Kane, R. L., McAlpine, D., Kathol, R. G., Fu, S. S., Hagedorn, H., & Wilt, T. J. (2008). Integration of mental health/substance abuse and primary care no. 173 (Prepared by the Minnesota Evidence-based Practice Center under contract no. 290-02-0009). AHRQ Publication No. 09-E003, 173, 1–362.Google Scholar
  30. 30.
    Postel, M. G., De Haan, H. A., & De Jong, C. A. J. (2008). E-therapy for mental health problems: A systematic review. Telemedicine and E-Health, 14(7), 707–714. Scholar
  31. 31.
    Mistry, S., Tanwar, S., & Tyagi, N. (2020). Blockchain for 5G-enabled IoT for industrial automation: A systematic review, solutions, and challenges. Mechanical Systems and Signal Processing, 135, 1–19.CrossRefGoogle Scholar
  32. 32.
    Tanwar, S., Vora, J., Kanriya, S., Tyagi, S., Kumar, N., Sharma, V., & You, I. (2019). Human arthritis analysis in fog computing environment using Bayesian Network Classifier and Thread Protocol. IEEE Consumer Electronics Magazine, 9(1), 88–94.CrossRefGoogle Scholar
  33. 33.
    Kaneriya, S., Chudasama, M., Tanwar, S., Tyagi, S., Kumar, N., & Rodrigues, J. J. P. C. (2019). Markov decision-based recommender system for sleep apnea patients. In IEEE conference on communications (IEEE ICC-2019) Shanghai, China, 20–24th May (pp. 1–6). Shanghai: IEEE.Google Scholar
  34. 34.
    Alzubi, J. A., Bharathikannan, B., Tanwar, S., Manikandan, R., Khanna, A., & Thaventhiran, C. (2019). Boosted neural network ensemble classification for lung cancer disease diagnosis. Applied Soft Computing Journal, 80, 579–591.CrossRefGoogle Scholar
  35. 35.
    Tanwar, S., Parekh, K., & Evans, R. (2019). Blockchain-based electronic healthcare record system for healthcare 4.0 applications. Journal of Information Security and Applications, 50, 1–14.Google Scholar
  36. 36.
    Gupta, R., Tanwar, S., Tyagi, S., Kumar, N., Obaidat, M. S., & Sadoun, B. (2019). HaBiTs: Blockchain-based telesurgery framework for healthcare 4.0. In International conference on computer, information and telecommunication systems (IEEE CITS-2019), Beijing, China, August 28–31 (pp. 6–10). Beijing: IEEE.Google Scholar
  37. 37.
    Gupta, R., Tanwar, S., Tyagi, S., & Kumar, N. (2019). Tactile Internet-based telesurgery system for healthcare 4.0: An architecture, research challenges, and future directions. IEEE Networks, 2019, 12–19.Google Scholar
  38. 38.
    Hathaliya, J., Tanwar, S., Tyagi, S., & Kumar, N. (2019). Securing electronics healthcare records in healthcare 4.0: A biometric-based approach. Computers & Electrical Engineering, 76, 398–410.CrossRefGoogle Scholar
  39. 39.
    Vora, J., Devmurari, P., Tanwar, S., Tyagi, S., Kumar, N., & Obaidat, M. S. (2018). Blind signatures based secured E-healthcare system. In International conference on computer, information and telecommunication systems (IEEE CITS-2018), Colmar, France, 11–13 July (pp. 177–181). Colmar: IEEE.Google Scholar
  40. 40.
    Vora, J., Italiya, P., Tanwar, S., Tyagi, S., Kumar, N., Obaidat, M. S., & Hsiao, K.-F. (2018). Ensuring privacy and security in E-health records. In International conference on computer, information and telecommunication systems (IEEE CITS-2018)h, Colmar, France, 11–13 July (pp. 192–196).Google Scholar
  41. 41.
    Vora, J., Tanwar, S., Tyagi, S., Kumar, N., & Rodrigues, J. P. C. (2017). FAAL: Fog computing-based patient monitoring system for ambient assisted living. In IEEE 19th international conference on e-health networking, applications and services (Healthcom-2017), Dalian University, Dalian, China, 12–15 October (pp. 1–6). Dalian: Dalian University.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Nurnadiah Zamri
    • 1
    Email author
  • Lazim Abdullah
    • 2
  • Mohd Asrul Hery Ibrahim
    • 3
  1. 1.Faculty of Informatics and ComputingUniversity Sultan Zainal AbidinBesutMalaysia
  2. 2.Faculty of Ocean Engineering Technology and InformaticsUniversity Malaysia TerengganuKuala NerusMalaysia
  3. 3.Faculty of Entrepreneurship and BusinessUniversity Malaysia KelantanKota BharuMalaysia

Personalised recommendations