A Systematic Review of Clinical Decision Support Systems in Alzheimer’s Disease Domain


  • Sherimon P.C. Arab Open University
  • Vinu Sherimon University of Technology and Applied Sciences
  • Preethii S.P.
  • Rahul Nair Royal Oman Police Hospital
  • Renchi Mathew Royal Oman Police Hospital




Dementia, Alzheimer’s disease, Clinical decision support system, Ontology, Knowledge base, Memory loss, Mental deterioration, Mental disorder


Dementia is one of the major public health issues faced by the world. Alzheimer’s disease (AD) is the most common form of dementia targeting old age groups around the world. It is a neurodegenerative condition with memory loss as its early symptom. Unfortunately, there is no cure for this disease currently. So various research in the medical and technical fields are being conducted to help people with Alzheimer’s. Many studies focus on early diagnosis of Alzheimer’s disease using clinical decision support system (CDSS) so that the progression of the disease can be slowed down to a great extent. In this context, we have undertaken a research to design and implement an ontology based Clinical decision support system for Alzheimer’s disease in Sultanate of Oman. A semantic knowledgebase (ontology) will be the core component of our Clinical decision support system. The objective of this research paper is two-fold (a) review the medical aspects of Alzheimer’s disease, and (b) review the available clinical decision support system based on ontology, robotics, and mobile applications in Alzheimer domain. Research articles published during 2011- 2020 in PubMed, Google scholar, Elsevier, SpringerLink and IEEE journals were reviewed. We found that there is various clinical decision support system which can aid physicians in suggesting diagnosis, and treatment of Alzheimer’s disease.




How to Cite

P.C., S., Sherimon, V., S.P., P., Nair, R., & Mathew, R. (2021). A Systematic Review of Clinical Decision Support Systems in Alzheimer’s Disease Domain. International Journal of Online and Biomedical Engineering (iJOE), 17(08), pp. 74–90. https://doi.org/10.3991/ijoe.v17i08.23643