Prognosis of Dementia Employing Machine Learning and Microsimulation Techniques: A Systematic Literature Review

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Abstract

OBJECTIVE: The objective of this paper is to investigate the goals and variables employed in the machine learning and microsimulation studies for the prognosis of dementia. METHOD: According to preset protocols, the Pubmed, Socups and Web of Science databases were searched to find studies that matched the defined inclusion/exclusion criteria, and then its references were checked for new studies. A quality checklist assessed the selected studies, and removed the low quality ones. The remaining ones (included set) had their data extracted and summarized. RESULTS: The summary of the data of the 37 included studies showed that the most common goal of the selected studies was the prediction of the conversion from mild cognitive impairment to Alzheimer's Disease, for studies that used machine learning, and cost estimation for the microsimulation ones. About the variables, neuroimaging was the most frequent used. CONCLUSIONS: The systematic literature review showed clear trends in prognosis of dementia research in what concerns machine learning techniques and microsimulation.

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Dallora, A. L., Eivazzadeh, S., Mendes, E., Berglund, J., & Anderberg, P. (2016). Prognosis of Dementia Employing Machine Learning and Microsimulation Techniques: A Systematic Literature Review. In Procedia Computer Science (Vol. 100, pp. 480–488). Elsevier B.V. https://doi.org/10.1016/j.procs.2016.09.185

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