Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review

Abstract

BackgroundDementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia.ObjectiveThe goal of this paper is to present evidence on the state of the art of studies investigating and the prognosis of dementia using machine learning and microsimulation techniques.MethodTo achieve our goal we carried out a systematic literature review, in which three large databases—Pubmed, Socups and Web of Science were searched to select studies that employed machine learning or microsimulation techniques for the prognosis of dementia. A single backward snowballing was done to identify further studies. A quality checklist was also employed to assess the quality of the evidence presented by the selected studies, and low quality studies were removed. Finally, data from the final set of studies were extracted in summary tables.ResultsIn total 37 papers were included. The data summary results showed that the current research is focused on the investigation of the patients with mild cognitive impairment that will evolve to Alzheimer’s disease, using machine learning techniques. Microsimulation studies were concerned with cost estimation and had a populational focus. Neuroimaging was the most commonly used variable.ConclusionsPrediction of conversion from MCI to AD is the dominant theme in the selected studies. Most studies used ML techniques on Neuroimaging data. Only a few data sources have been recruited by most studies and the ADNI database is the one most commonly used. Only two studies have investigated the prediction of epidemiological aspects of Dementia using either ML or MS techniques. Finally, care should be taken when interpreting the reported accuracy of ML techniques, given studies’ different contexts.

Technology and Dementia: The Future is Now

“…In terms of dementia diagnosis, there have been increasing applications of various machine learning approaches, most commonly with imaging data for diagnosis and disease progression [11]. This includes amyloid PET imaging [12], MR imaging [13] and combined PET and MR imaging [14].…”

Multifactorial 10-Year Prior Diagnosis Prediction Model of Dementia

“…Being able to work with large amounts of multifactorial data, machine learning technology could be beneficial in the investigation of prognostic estimates for dementia. However, after a systematic literature review that explored machine learning approaches for prognosis [14], it was concluded that, based upon 37 primary studies, the research has been very focused on neuroimaging for identifying and validating biomarkers in the brain to predict the development of dementia from its prodromal stage, i.e., mild cognitive impairment (MCI), and usually in a time frame of 3 years [14]. These studies scarcely considered modifiable risk factors.…”

P5 eHealth: An Agenda for the Health Technologies of the Future

“…In their review, Dallora and colleagues (Dallora et al 2017) found that for the prognosis of dementia, the most applied ML technique is SVM; among ML, neuroimaging studies (i.e. MRI and PET) were most frequent compared to cognitive/ behavioural, genetic, lab tests and demographic data with the main of predicating the proportion of MCI individuals that will develop AD.…”

“…The researchers in terms of validation procedure, datasets used, number or records within the same dataset and follow-up period found limitations. However, defining biomarkers in the field of neurodegenerative diseases could improve diagnosis and treatment and consolidate the role of precision medicine and prediction of disease progress (Dallora et al 2017;Reitz 2016;Rosenberg 2017). As we have seen, technologies (and eHealth especially) could be a good instrument, also in the context of the P5 approach and future medicine.…”