This methodology produces decision trees that target population groups more prone to suffering from mild cognitive impairment and are useful for cost-effective selective screening of the disease.
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Method Article
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
In This Article
Summary
Abstract
Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common MCI tests are time-consuming such that indiscriminate massive screening would not be cost-effective. Here, we describe a protocol that uses machine learning techniques to rapidly select candidates for further screening via a question-based MCI test. This minimizes the number of resources required for screening because only patients who are potentially MCI positive are tested further.
This methodology was applied in an initial MCI research study that formed the starting point for the design of a selective screening decision tree. The initial study collected many demographic and lifestyle variables as well as details about patient medications. The Short Portable Mental Status Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE) were used to detect possible cases of MCI. Finally, we used this method to design an efficient process for classifying individuals at risk of MCI. This work also provides insights into lifestyle-related factors associated with MCI that could be leveraged in the prevention and early detection of MCI among elderly populations.
Introduction
Population aging is increasing the prevalence of chronic and degenerative diseases, especially degenerative dementias, which are expected to affect more than 131 million people worldwide by 20501. Among all the degenerative dementias, Alzheimer's disease (AD) is the most common with an overall prevalence in Europe of 6.88%2. Due to the ever-declining independence of AD patients, this group should start receiving support as soon as AD starts to manifest. Therefore, the early detection of prodromal signs of AD, such as mild cognitive impairment (MCI), is essential.
MCI is defined as an inter....
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Protocol
The methodology applied in this study has been previously published5 in work carried out at the University CEU Cardenal Herrera together with community pharmacies in the region of Valencia (Spain) associated with the Spanish Society of Family and Community Pharmacy (SEFAC). This current study was reviewed and approved by the Research Ethics Committee at the Universidad CEU Cardenal Herrera (approval no. CEI11/001) in March 2011. All individuals involved in the study gave their written informed consent to participation in accordance with the Declaration of Helsinki.
1. Selection of factors associated with mild cognitive....
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Results
The participating pharmacies gathered data from 728 users and collected demographic variables in addition to the drugs prescribed to the participants. A univariate logistic regression was performed for all the variables34; the error bar graphs shown in Figure 3 and Figure 4 are convenient graphical representations of the confidence interval of the odds ratio (for qualitative variables) and the confidence i.......
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Discussion
After searching for terms associated with MCI in Cochrane studies in the PubMed database, a specific questionnaire was created for this study that used the most evident variables with a proven association with MCI. Demographic, lifestyle, and social factors, as well as the patient's pharmacotherapy and some relevant pathologies were also recorded. Additionally, the SPMSQ and MMSE MCI tests were also selected. Importantly, the SPMSQ was not affected by participants' level of schooling. Pharmacists were trained to .......
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Disclosures
The authors have nothing to disclose.
Acknowledgements
This work was made possible by the support of the Know Alzheimer Foundation and help from the multimedia production service at the Universidad CEU Cardenal Herrera, especially Enrique Giner. We would like to recognize the work of all the participating pharmacies (SEFAC), and the collaborating doctors from the Society of Primary Care Doctors (SEMERGEN) and Neurology Society (SVN) who helped with the MCI diagnoses, especially Vicente Gassull, Rafael Sánchez, and Jordi Pérez. Finally, we thank all those who agreed to take part in this study.
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Materials
| Name | Company | Catalog Number | Comments |
|---|---|---|---|
| caret | Max Kuhn | R package | |
| rpart | Terry Therneau, Beth Atkinson, Brian Ripley | R package | |
| SPMSQ in Spanish | Farmaceuticoscomunitarios.org | http://farmaceuticoscomunitarios.org/anexos/vol11_n1/ANEXO1.pdf | |
| SPMSQ in English | geriatrics.stanford.edu | https://geriatrics.stanford.edu/culturemed/overview/assessment/assessment_toolkit/spmsq.html | |
| MMSE in Spanish | Farmaceuticoscomunitarios.org | http://farmaceuticoscomunitarios.org/anexos/vol11_n1/ANEXO2.pdf | |
| MMSE in English | oxfordmedicaleducation.com | http://www.oxfordmedicaleducation.com/geriatrics/mini-mental-state-examination-mmse/ |
References
- Prince, M., Comas-Herrera, A., Knapp, M., Guerchet, M., Karagiannidou, M. World Alzheimer report 2016: improving healthcare for people living with dementia: coverage, quality and costs now and in the future. , Alzheimer's Disease International (ADI). https://scholar.google.com/scholar.bib?q=info:mEGpcpLHEIMJ:scholar.google.com (2016).
- Niu, H., Álvarez-Álvarez, I., Guillén-Grima, F., Aguinaga-Ontoso, I. Prevalence and incidence of Alzheimer's disease....
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