SchizoBot: Delivering Cognitive Behavioural Therapy for Augmented Management of Schizophrenia


Nwoye, E. O., Muslehat, A. A., Umeh, C., Okodeh, S. O., & Woo, W. L. (2024). SchizoBot: Delivering Cognitive Behavioural Therapy for Augmented Management of Schizophrenia. Digital Technologies Research and Applications, 3(2), 24–40.


  • Ephraim O Nwoye
    Department of Biomedical Engineering, University of Lagos, Lagos, 101017, Nigeria
  • Abdulgafaar A Muslehat Department of Biomedical Engineering, University of Lagos, Lagos, 101017, Nigeria
  • Charles Umeh Department of Psychiatry, Lagos University Teaching Hospital/CMUL, Lagos, 102215, Nigeria
  • Samuel O Okodeh Department of Biomedical Engineering, University of Lagos, Lagos, 101017, Nigeria
  • Wai Lok Woo Department of Computer and Information Sciences, Northumbria University, London, E1 7HT, United Kingdom

According to WHO, about 1.86 million people in Nigeria and about 24 million people worldwide are living with schizophrenia, having symptoms varying from hallucination to delusion, and distorted speech and thinking. Schizophrenia is a life-long disorder with no cure and thus, patients need continuous management with medications and psychotherapy. However, due to various factors such as the cost of therapy, time consumption, lack of adequate health workers, the unwillingness of patients to engage, and the pandemic, there is a need for an effective alternate medium for providing cognitive behavioural therapy (CBT) to schizophrenia patients. This research aims to develop a chatbot, which is called SchizoBot, delivering CBT for augmented management of schizophrenia. CBT for schizophrenia details, along with FAQs of schizophrenia patients were collected and adopted into a conversational format for pre-processing and model development. The model was developed with artificial neural network (ANN) and trained with the dataset which was split into train-test data to optimize the performance of the model. The result of the ANN showed an accuracy score of 93.97% at 60:40 train-test data split with 200 epochs. This robust system which provides an optimized chatbot platform using ANN as the model classifier for CBT delivery is foreseen to be a windfall to clinicians and patients as an augmentative management tool for schizophrenia. This, therefore, is a relatively low-cost and easily accessible means to significantly improve the health of schizophrenia patients while assisting clinicians in therapy delivery and compensating for the lapses in the administration of CBT to schizophrenia patients.


cognitive behavioural therapy; schizophrenia; SchizoBot; artificial neural network; artificial intelligence


  1. Möller, H.J. The relevance of negative symptoms in schizophrenia and how to treat them with psychopharmaceuticals. Psychiatr. Danub. 2016, 28, 435–440.
  2. Quaedflieg, C.W.E.M., Smeets, T. Stress vulnerability models. In Encyclopedia of Behavioral Medicine; Springer: New York, US, 2013; pp. 1897–1900.
  3. McCutcheon, R.A.; Reis Marques, T.; Howes, O.D. Schizophrenia—An overview. JAMA Psychiat. 2020, 77, 201–210.
  4. Birchwood, M.; Michail, M.; Meaden, A.; Tarrier, N.; Lewis, S.; Wykes, T.; Davies, L.; Dunn, G.; Peters, E. Cognitive behaviour therapy to prevent harmful compliance with command hallucinations (COMMAND): A randomised controlled trial. Lancet Psychiat. 2014, 1, 23–33.
  5. Jauhar, S.; Laws, K.R.; McKenna, P.J. CBT for schizophrenia: A critical viewpoint. Psychol. Med. 2019, 49, 1233–1236.
  6. Beck, A.T.; Rector, N.A. A cognitive model of hallucinations. Cogn. Ther. Res. 2003, 27, 19–52.
  7. Batinic, B. Cognitive models of positive and negative symptoms of schizophrenia and implications for treatment. Psychiatr. Danub. 2019, 31(Suppl. 2), S181–S184.
  8. Fitzpatrick, K.K.; Darcy, A.; Vierhile, M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Ment. Health. 2017, 4, e19.
  9. Inkster, B.; Sarda, S.; Subramanian, V. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: Real-world data evaluation mixed-methods study. JMIR Mhealth Uhealth. 2018, 6, e12106.
  10. Mehta, A.; Niles, A.N.; Vargas, J.H.; Marafon, T.; Couto, D.D.; Gross, J.J. Acceptability and effectiveness of artificial intelligence therapy for anxiety and depression (Youper): Longitudinal observational study. J. Med. Internet Res. 2021, 23, e26771.
  11. 2019 IEEE 16th India Council International Conference (INDICON). IEEE. Available online: (accessed on 8 March 2024).
  12. Schizophrenia. Available online: (accessed on 14 April 2022).
  13. Nwoye, E.; Woo, W.L.; Fidelis, O.; Umeh, C.; Gao, B. Development and investigation of cost-sensitive pruned decision tree model for improved schizophrenia diagnosis. Int. J. Auto. AI Mach. Learn. 2020, 1, 17–41.
  14. Parry Chatbot: AI Chatbot that Simulating Paranoia PERSON. Available online: (accessed on 8 March 2024).
  15. Lahoz-Beltra, R.; Rodriguez, R.J. Modeling a cancerous tumor development in a virtual patient suffering from a depressed state of mind: Simulation of somatic evolution with a customized genetic algorithm. Biosystems 2020, 198, 104261.
  16. Lahoz-Beltra, R.; López, C.C. LENNA (Learning Emotions Neural Network Assisted): An Empathic Chatbot Designed to Study the Simulation of Emotions in a Bot and Their Analysis in a Conversation. Computers 2021, 10, 170.