Advanced analytical methodologies for measuring healthy ageing and its determinants, using factor analysis and machine learning techniques: the ATHLOS project. Caballero, F. F., Soulis, G., Engchuan, W., Sánchez-Niubó, A., Arndt, H., Ayuso-Mateos, J. L., Haro, J. M., Chatterji, S., & Panagiotakos, D. B. Scientific Reports, 7(1):43955, March, 2017. Number: 1 Publisher: Nature Publishing Group![Advanced analytical methodologies for measuring healthy ageing and its determinants, using factor analysis and machine learning techniques: the ATHLOS project [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex A most challenging task for scientists that are involved in the study of ageing is the development of a measure to quantify health status across populations and over time. In the present study, a Bayesian multilevel Item Response Theory approach is used to create a health score that can be compared across different waves in a longitudinal study, using anchor items and items that vary across waves. The same approach can be applied to compare health scores across different longitudinal studies, using items that vary across studies. Data from the English Longitudinal Study of Ageing (ELSA) are employed. Mixed-effects multilevel regression and Machine Learning methods were used to identify relationships between socio-demographics and the health score created. The metric of health was created for 17,886 subjects (54.6% of women) participating in at least one of the first six ELSA waves and correlated well with already known conditions that affect health. Future efforts will implement this approach in a harmonised data set comprising several longitudinal studies of ageing. This will enable valid comparisons between clinical and community dwelling populations and help to generate norms that could be useful in day-to-day clinical practice.
@article{caballero_advanced_2017,
title = {Advanced analytical methodologies for measuring healthy ageing and its determinants, using factor analysis and machine learning techniques: the {ATHLOS} project},
volume = {7},
copyright = {2017 The Author(s)},
issn = {2045-2322},
shorttitle = {Advanced analytical methodologies for measuring healthy ageing and its determinants, using factor analysis and machine learning techniques},
url = {https://www.nature.com/articles/srep43955},
doi = {10.1038/srep43955},
abstract = {A most challenging task for scientists that are involved in the study of ageing is the development of a measure to quantify health status across populations and over time. In the present study, a Bayesian multilevel Item Response Theory approach is used to create a health score that can be compared across different waves in a longitudinal study, using anchor items and items that vary across waves. The same approach can be applied to compare health scores across different longitudinal studies, using items that vary across studies. Data from the English Longitudinal Study of Ageing (ELSA) are employed. Mixed-effects multilevel regression and Machine Learning methods were used to identify relationships between socio-demographics and the health score created. The metric of health was created for 17,886 subjects (54.6\% of women) participating in at least one of the first six ELSA waves and correlated well with already known conditions that affect health. Future efforts will implement this approach in a harmonised data set comprising several longitudinal studies of ageing. This will enable valid comparisons between clinical and community dwelling populations and help to generate norms that could be useful in day-to-day clinical practice.},
language = {en},
number = {1},
urldate = {2020-07-08},
journal = {Scientific Reports},
author = {Caballero, Francisco Félix and Soulis, George and Engchuan, Worrawat and Sánchez-Niubó, Albert and Arndt, Holger and Ayuso-Mateos, José Luis and Haro, Josep Maria and Chatterji, Somnath and Panagiotakos, Demosthenes B.},
month = mar,
year = {2017},
pmcid = {PMC5345043},
pmid = {28281663},
note = {Number: 1
Publisher: Nature Publishing Group},
keywords = {ML, Machine Learning},
pages = {43955},
}
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