A revision and analysis of the comprehensiveness of the main longitudinal studies of human aging for data mining research

Ribeiro, Caio; Brito, Luis Henrique S.; Nobre, Cristiane Neri; Freitas, Alex A.; Zárate, Luis Enrique

doi:10.1002/widm.1202

Cited by 8 publications

(6 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, literature shows that other types of brain modalities such as biological, clinical, neuroimaging, and genetic data could provide valuable information about the changes in the functionality and structure of the brain across time. It is recommended that in future studies the integration of these modalities be considered [20,21]. In contrast, one strength of our study is the long-term follow-up across a total time frame of 12 years.…”

Section: Plos Onementioning

confidence: 99%

“…As such, including a large number of prognostic factors at the same time would reduce these confounding effects [19]. Furthermore, most previous studies have used statistical methods to study cognitive function [20,21]. However, statistical methods are limited in their hypothesis and do not provide an assessment of the relative rankings of the potential predictors [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predictors of the rate of cognitive decline in older adults using machine learning

et al. 2023

View full text Add to dashboard Cite

Background The longitudinal rates of cognitive decline among aging populations are heterogeneous. Few studies have investigated the possibility of implementing prognostic models to predict cognitive changes with the combination of categorical and continuous data from multiple domains. Objective Implement a multivariate robust model to predict longitudinal cognitive changes over 12 years among older adults and to identify the most significant predictors of cognitive changes using machine learning techniques. Method In total, data of 2733 participants aged 50–85 years from the English Longitudinal Study of Ageing are included. Two categories of cognitive changes were determined including minor cognitive decliners (2361 participants, 86.4%) and major cognitive decliners (372 participants, 13.6%) over 12 years from wave 2 (2004–2005) to wave 8 (2016–2017). Machine learning methods were used to implement the predictive models and to identify the predictors of cognitive decline using 43 baseline features from seven domains including sociodemographic, social engagement, health, physical functioning, psychological, health-related behaviors, and baseline cognitive tests. Results The model predicted future major cognitive decliners from those with the minor cognitive decline with a relatively high performance. The overall AUC, sensitivity, and specificity of prediction were 72.84%, 78.23%, and 67.41%, respectively. Furthermore, the top 7 ranked features with an important role in predicting major vs minor cognitive decliners included age, employment status, socioeconomic status, self-rated memory changes, immediate word recall, the feeling of loneliness, and vigorous physical activity. In contrast, the five least important baseline features consisted of smoking, instrumental activities of daily living, eye disease, life satisfaction, and cardiovascular disease. Conclusion The present study indicated the possibility of identifying individuals at high risk of future major cognitive decline as well as potential risk/protective factors of cognitive decline among older adults. The findings could assist in improving the effective interventions to delay cognitive decline among aging populations.

show abstract

Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictors of the rate of cognitive decline in older adults using machine learning

et al. 2023

View full text Add to dashboard Cite

show abstract

“…There are several possible approaches for considering temporal patterns in supervised machine learning problems [Ribeiro et al, 2017], such as creating temporal features in a preprocessing step [Ribeiro and Freitas, 2021a], Structural Pattern Detection [Morid et al, 2020], Recurrent Neural Networks (often Long-Short Term Memory) [Aghili et al, 2018], and Deep Learning [Luo et al, 2020]. In this section we focus on decision tree-based classifiers, which are popular in biomedical applications and are the focus of our proposed algorithm adaptation for longitudinal classification.…”

Section: Related Workmentioning

confidence: 99%

A lexicographic optimisation approach to promote more recent features on longitudinal decision-tree-based classifiers: Applications to the English Longitudinal Study of Ageing

Ribeiro

Freitas

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Supervised machine learning algorithms rarely cope directly with the temporal information inherent to longitudinal datasets, which have multiple measurements of the same feature across several time points and are often generated by large health studies. In this paper we report on experiments which adapt the feature-selection function of decision tree-based classifiers to consider the temporal information in longitudinal datasets, using a lexicographic optimisation approach. This approach gives higher priority to the usual objective of maximising the information gain ratio, and it favours the selection of features more recently measured as a lower priority objective. Hence, when selecting between features with equivalent information gain ratio, priority is given to more recent measurements of biomedical features in our datasets. We performed experiments with 20 longitudinal datasets created from a human ageing study, and concluded from our results that this lexicographic optimisation approach is worthwhile and computationally inexpensive. In addition to a slight improve in predictive accuracy, the changed feature-selection function promotes models based on information that is more directly related to the subject's current biomedical situation and, thus, more actionable.

show abstract

“…Со-гласно литературным данным, симптомы тре-воги и депрессии наблюдаются у 25-30 % лиц старше 65 лет, причем на фоне сопутствующих соматических заболеваний эта цифра достигает 50 % [7,8].…”

Section: депрессияunclassified

The prevalence of geriatric syndromes in general practice

Киселева

Валерьевна²,

Rafalskaya

et al. 2017

Rossiĭskiĭ semeĭnyĭ vrach

View full text Add to dashboard Cite

The article presents data on prevalence of the main geriatric syndromes in general medical practice. The method of the structured approach to the assessment of the problems of elderly and old patients that contributes to the early detection of the main geriatric syndromes and also includes a complex as well as interdisciplinary assessment of the state of the physical health, state of mental health, social relations, satisfaction with the quality of life is comprehensively considered. The necessity of applying this methodology in general medical practice is considered.

show abstract

A revision and analysis of the comprehensiveness of the main longitudinal studies of human aging for data mining research

Cited by 8 publications

References 71 publications

Predictors of the rate of cognitive decline in older adults using machine learning

Predictors of the rate of cognitive decline in older adults using machine learning

A lexicographic optimisation approach to promote more recent features on longitudinal decision-tree-based classifiers: Applications to the English Longitudinal Study of Ageing

The prevalence of geriatric syndromes in general practice

Contact Info

Product

Resources

About