Reference : Machine learning-based prediction of frailty in elderly people : Data from the Berlin...

	Document type :	Scientific congresses, symposiums and conference proceedings : Poster
	Discipline(s) :	Human health sciences : Geriatrics
	Focus Areas :	Systems Biomedicine
	To cite this reference:	http://hdl.handle.net/10993/54555

Title :	Machine learning-based prediction of frailty in elderly people : Data from the Berlin Aging Study II (BASE-II)
Language :	English
Author, co-author :	Didier, Jeff [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM) >]
	de Landtsheer, Sébastien [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM) >]
	Pires Pacheco, Maria Irene [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM) >]
	Kishk, Ali [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM) >]
	Schneider, Jochen [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Medical Translational Research >]
	Demuth, Ilja [Charité – Universitätsmedizin Berlin > Department of Endocrinology and Metabolic Diseases]
	Sauter, Thomas [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM) >]
Publication date :	9-Oct-2022
Number of pages :	A0
Peer reviewed :	No
Audience :	International
Event name :	21st International Conference on Systems Biology
Event date :	from 08-10-2022 to 12-10-2022
Event organizer :	Humboldt-Universität zu Berlin
Event place (city) :	Berlin
Event country :	Germany
Keywords :	[en] frailty ; aging ; machine learning ; prediction ; biomarker ; risk factors
Abstract :	[en] Frailty is a geriatric medical condition that is highly associated with age and age-related diseases. The multidimensional consequences of frailty are heavily impacting the quality of life, and will inevitably increase the burden on healthcare systems in the future. Most importantly, the lack of a universal standard to describe, diagnose, or let alone treat frailty, is further complicating the situation in the long-term. Nowadays, more and more frailty assessment tools are being developed on a regional and institutional basis, which is continuing to drive the heterogeneity in the characterization of frailty further apart. Gaining better insights into the underlying causes and pathophysiology of frailty, and how it is developing in patients is, therefore, required to establish strong and accurately tailored response schemes for frail patients, where currently only symptoms are treated. Thus, in this study, we deployed machine learning-based classification and optimization techniques to predict frailty in the Berlin Aging Study II (BASE-II, N=1512, frail=484) and revealed some of the most informative biomedical information to characterize frailty, including new potential biomarkers. Frailty in BASE-II was measured by the Fried et al. 5-item frailty index, composed of the clinical variables grip strength, weight loss, exhaustion, physical activity, and gait. The level of frailty in BASE-II was adapted for binary classification purposes by merging the pre-frail and frail levels as frail. A configurable in-house pipeline was developed for pre-processing the clinical data, predicting the target disease, and determining the most informative subgroup of clinical measurements with regards to frailty. The best prediction power was yielded with resampling and dimensionality reduction techniques using the F-beta-2 score, and was further increased by adding one item of the Fried et al. frailty index. We suggest that a combination of the easy-to-obtain biomedical information on frailty risk factors together with one Fried et al. phenotype information provided by i.e. smart wearable devices (gait, grip strength, . . . ) could significantly improve the frailty prediction power.
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/10993/54555
FnR project :	FnR ; FNR12252781 > Andreas Zilian > DRIVEN > Data-driven Computational Modelling And Applications > 01/09/2018 > 28/02/2025 > 2017

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	Fulltext file(s): File Commentary Version Size Access Limited access Poster_JD_ICSB.pdf Author preprint 3.18 MB Request a copy Additional material(s): File Commentary Size Access Limited access 137_Didier.pdf Flashtalk Presentation 691.77 kB Request a copy

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