PP-LCGM
Cross-source consensus on PP-LCGM from 1 sources and 4 claims.
1 sources · 4 claims
Uses
How it works
Benefits
Background
Highlighted claims
- PP-LCGM simultaneously analyses multiple longitudinal outcomes across latent classes. — Machine learning-driven health profiling and multidimensional trajectory analysis in first-ever ischaemic stroke: protocol for a multicentre cross-sectional and prospective longitudinal study
- PP-LCGM identifies shared trajectory patterns among interrelated variables while capturing individual growth parameters and cross-domain synergies. — Machine learning-driven health profiling and multidimensional trajectory analysis in first-ever ischaemic stroke: protocol for a multicentre cross-sectional and prospective longitudinal study
- The model maps joint developmental trajectories of the three highest-ranked health attributes over six months. — Machine learning-driven health profiling and multidimensional trajectory analysis in first-ever ischaemic stroke: protocol for a multicentre cross-sectional and prospective longitudinal study
- PP-LCGM has roots in psychological research and behavioural sciences but is underexplored in stroke rehabilitation. — Machine learning-driven health profiling and multidimensional trajectory analysis in first-ever ischaemic stroke: protocol for a multicentre cross-sectional and prospective longitudinal study