Decision tree for early detection of cognitive impairment by community pharmacists
| Autor: | María Teresa Climent, F. J. Muñoz-Almaraz, Lucrecia Moreno, Maria Dolores Guerrero, Juan Pardo |
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| Přispěvatelé: | Producción Científica UCH 2018, UCH. Departamento de Farmacia, UCH. Departamento de Matemáticas, Física y Ciencias Tecnológicas |
| Rok vydání: | 2018 |
| Předmět: |
Gerontology
Farmacias media_common.quotation_subject Memory disorders - Diagnosis Decision tree Recursive partitioning 03 medical and health sciences 0302 clinical medicine mild cognitive impairment community pharmacists Reading (process) Health care mental disorders memory complaint Memory disorders Medicine Dementia risk factors Pharmacology (medical) early detection media_common Original Research Pharmacology decision trees business.industry lcsh:RM1-950 Drugstores Pharmaceutical services Predictive analytics medicine.disease Educational attainment 030227 psychiatry Test (assessment) statistical learning lcsh:Therapeutics. Pharmacology sleep duration business Memoria - Trastornos Atención farmacéutica 030217 neurology & neurosurgery |
| Zdroj: | CEU Repositorio Institucional Fundación Universitaria San Pablo CEU (FUSPCEU) Frontiers in Pharmacology Frontiers in Pharmacology, Vol 9 (2018) |
| DOI: | 10.3389/fphar.2018.01232 |
| Popis: | Este artículo se encuentra disponible en la página web de la revista en la siguiente URL: https://www.frontiersin.org/articles/10.3389/fphar.2018.01232/full Purpose: The early detection of Mild Cognitive Impairment (MCI) is essential in aging societies where dementia is becoming a common manifestation among the elderly. Thus our aim is to develop a decision tree to discriminate individuals at risk of MCI among non-institutionalized elderly users of community pharmacy. A more clinically and patient-oriented role of the community pharmacist in primary care makes the dispensation of medication an adequate situation for an effective, rapid, easy, and reproducible screening of MCI. Methods: A cross-sectional study was conducted with 728 non-institutionalized participants older than 65. A total of 167 variables were collected such as age, gender, educational attainment, daily sleep duration, reading frequency, subjective memory complaint, and medication. Two screening tests were used to detect possible MCI: Short Portable Mental State Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE). Participants classified as positive were referred to clinical diagnosis. A decision tree and predictive models are presented as a result of applying techniques of machine learning for a more efficient enrollment. Results: One hundred and twenty-eight participants (17.4%) scored positive on MCI tests. A recursive partitioning algorithmwith themost significant variables determined that the most relevant for the decision tree are: female sex, sleeping more than 9 h daily, age higher than 79 years as risk factors, and reading frequency. Moreover, psychoanaleptics, nootropics, and antidepressants, and anti-inflammatory drugs achieve a high score of importance according to the predictive algorithms. Furthermore, results obtained from these algorithms agree with the current research on MCI. Conclusion: Lifestyle-related factors such as sleep duration and the lack of reading habits are associated with the presence of positive in MCI test. Moreover, we have depicted how machine learning provides a sound methodology to produce tools for early detection of MCI in community pharmacy. Impact of findings on practice: The community of pharmacists provided with adequate tools could develop a crucial task in the early detection of MCI to redirect them immediately to the specialists in neurology or psychiatry. Pharmacists are one of the most accessible and regularly visited health care professionals and they can play a vital role in early detection of MCI. |
| Databáze: | OpenAIRE |
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