In this paper, we focus on an interpretable and visible approach to detect causal relationship networks in order to study risk factors of older adult falls.
Implied causal relationships may have much the same force as explicitly stated relationships, as shown in the following sentences where both the temporal ...
TL-PC: An Interpretable Causal Relationship Networks on Older Adults Fall Influence Factors ... Relationship Networks on Older Adults Fall Influence Factors.
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TL-PC: An Interpretable Causal Relationship Networks on Older Adults Fall Influence Factors ... Neural Networks Learn. Syst. 2017; Readers: Everyone. Crime ...
This study employs a novel approach—a mixed undirected graphical model (MUGM)—to unravel the interplay between sociodemographics, mental well-being, body ...
Objectives: The aim of the study was to investigate fear of falling, kinesiophobia, and sensory processing in older adults with hypertension and ...
Feb 2, 2022 · In this 3-year longitudinal study, we evaluated a predictive model for risk of fall among community-dwelling older adults using machine learning methods.
Missing: TL- PC: Causal Relationship Networks Influence
TL-PC: An Interpretable Causal Relationship Networks on Older Adults Fall Influence Factors · Medicine, Engineering. 2018 IEEE International Conference on Big…
【24h】13.TL-PC: An Interpretable Causal Relationship Networks on Older Adults Fall Influence Factors. Zihan Li,Wei Ding,Kui Yu,Suzanne G. Leveille,Pin Chen.